Physics-informed machine learning in prognostics and health management: State of the art and challenges
•Systematic bibliometric analysis of PIML in PHM.•Novel perspectives for PIML from the “Informed knowledge forms” and “Informed methods”.•Taxonomy of PIML approaches in PHM.•Highlight remaining challenges and future perspectives based on this review. Prognostics and health management (PHM) plays a c...
Uloženo v:
| Vydáno v: | Applied mathematical modelling Ročník 124; s. 325 - 352 |
|---|---|
| Hlavní autoři: | , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Elsevier Inc
01.12.2023
Elsevier |
| Témata: | |
| ISSN: | 0307-904X, 1872-8480 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | •Systematic bibliometric analysis of PIML in PHM.•Novel perspectives for PIML from the “Informed knowledge forms” and “Informed methods”.•Taxonomy of PIML approaches in PHM.•Highlight remaining challenges and future perspectives based on this review.
Prognostics and health management (PHM) plays a constructive role in the equipment’s entire life health service. It has long benefited from intensive research into physics modeling and machine learning methods. However, in practice, the existing solutions often encounter difficulties caused by sparse data & incomplete system failure knowledge. Pure machine learning or physics-based methods can sometimes be infeasible in such situations. As a result, there has been a growing interest in developing physics-informed machine learning (PIML) models which allow incorporating different forms of physics knowledge at different positions of the machine learning pipeline. This combination provides significant assistance for detection, diagnostic, and prognostics. However, to the best of our knowledge, the bibliometrics analyses and the comprehensive review of the existing research concerning PIML in PHM remain vacant. Our review is therefore dedicated to filling these gaps. We synthesize the concept of PIML in PHM, and propose a taxonomy of PIML approaches from the perspective of “Expression forms of informed knowledge” and “Knowledge informed methods”. The findings and discussions presented in this paper enable us to clarify the current state of the art and the emerging opportunities of PIML approaches, especially for building PHM systems that can work under the “small data and scarce physics knowledge” paradigm. |
|---|---|
| AbstractList | Prognostics and health management (PHM) plays a constructive role in the equipment’s entire life health service. It has long benefited from intensive research into physics modeling and machine learning methods. However, in practice, the existing solutions often encounter difficulties caused by sparse data & incomplete system failure knowledge. Pure machine learning or physics-based methods can sometimes be infeasible in such situations. As a result, there has been a growing interest in developing physics-informed machine learning (PIML) models which allow incorporating different forms of physics knowledge at different positions of the machine learning pipeline. This combination provides significant assistance for detection, diagnostic, and prognostics. However, to the best of our knowledge, the bibliometrics analyses and the comprehensive review of the existing research concerning PIML in PHM remain vacant. Our review is therefore dedicated to filling these gaps. We synthesize the concept of PIML in PHM, and propose a taxonomy of PIML approaches from the perspective of “Expression forms of informed knowledge” and “Knowledge informed methods”. The findings and discussions presented in this paper enable us to clarify the current state of the art and the emerging opportunities of PIML approaches, especially for building PHM systems that can work under the “small data and scarce physics knowledge” paradigm. •Systematic bibliometric analysis of PIML in PHM.•Novel perspectives for PIML from the “Informed knowledge forms” and “Informed methods”.•Taxonomy of PIML approaches in PHM.•Highlight remaining challenges and future perspectives based on this review. Prognostics and health management (PHM) plays a constructive role in the equipment’s entire life health service. It has long benefited from intensive research into physics modeling and machine learning methods. However, in practice, the existing solutions often encounter difficulties caused by sparse data & incomplete system failure knowledge. Pure machine learning or physics-based methods can sometimes be infeasible in such situations. As a result, there has been a growing interest in developing physics-informed machine learning (PIML) models which allow incorporating different forms of physics knowledge at different positions of the machine learning pipeline. This combination provides significant assistance for detection, diagnostic, and prognostics. However, to the best of our knowledge, the bibliometrics analyses and the comprehensive review of the existing research concerning PIML in PHM remain vacant. Our review is therefore dedicated to filling these gaps. We synthesize the concept of PIML in PHM, and propose a taxonomy of PIML approaches from the perspective of “Expression forms of informed knowledge” and “Knowledge informed methods”. The findings and discussions presented in this paper enable us to clarify the current state of the art and the emerging opportunities of PIML approaches, especially for building PHM systems that can work under the “small data and scarce physics knowledge” paradigm. |
| Author | MEDJAHER, Kamal GOGU, Christian MORIO, Jérôme NGUYEN, Khanh T.P. DENG, Weikun |
| Author_xml | – sequence: 1 givenname: Weikun orcidid: 0000-0002-5195-4184 surname: DENG fullname: DENG, Weikun email: weikun.deng@enit.fr organization: Laboratoire Génie de Production, LGP, Université de Toulouse, INP-ENIT, 47 Av. d’Azereix, Tarbes, 65016, France – sequence: 2 givenname: Khanh T.P. surname: NGUYEN fullname: NGUYEN, Khanh T.P. organization: Laboratoire Génie de Production, LGP, Université de Toulouse, INP-ENIT, 47 Av. d’Azereix, Tarbes, 65016, France – sequence: 3 givenname: Kamal surname: MEDJAHER fullname: MEDJAHER, Kamal organization: Laboratoire Génie de Production, LGP, Université de Toulouse, INP-ENIT, 47 Av. d’Azereix, Tarbes, 65016, France – sequence: 4 givenname: Christian surname: GOGU fullname: GOGU, Christian organization: Institut Clément Ader (ICA), Université de Toulouse, ISAE-SUPAERO, UPS, CNRS, INSA, Mines Albi, 3 rue Caroline Aigle, Toulouse 31400, France – sequence: 5 givenname: Jérôme surname: MORIO fullname: MORIO, Jérôme organization: ONERA/DTIS, Université de Toulouse, Toulouse F-31055, France |
| BackLink | https://hal.science/hal-04290849$$DView record in HAL |
| BookMark | eNp9kMFq3DAQhkVJoZu0D9CbrjnYlSzbspNTCEkTWGggLfQmZqWRrcWWFkkE8vbVdksPOeQ0zPB9M8x_Ts588EjIV85qznj_bV_DYa0b1oiayZpx_oFs-CCbamgHdkY2TDBZjaz9_Ymcp7RnjHWl25DpaX5NTqfKeRviioauoGfnkS4I0Ts_UefpIYbJh5QLSMEbOiMseS6ohwlX9PmKPmfISIOleUYKMf_l9AzLgn7C9Jl8tLAk_PKvXpBf93c_bx-q7Y_vj7c320oL2eZK4453Ru4aC1Kg7AfRS9NJGHmPWjQCTW92YE0HtoVuNw6DhqHvpO3LoLNCXJDL095yWR2iWyG-qgBOPdxs1XHG2mZkQzu-8MLyE6tjSCmi_S9wpo6pqr0qqapjqopJVVItjnzjaFc-d8HnCG5517w-mVjef3EYVdIOvUbjIuqsTHDv2H8Ayw6VnA |
| CitedBy_id | crossref_primary_10_1016_j_ress_2025_111730 crossref_primary_10_1088_1361_6501_add48e crossref_primary_10_1016_j_ress_2025_111379 crossref_primary_10_1016_j_ymssp_2024_112192 crossref_primary_10_1115_1_4065483 crossref_primary_10_1016_j_cnsns_2024_107911 crossref_primary_10_3390_en18174637 crossref_primary_10_1016_j_apenergy_2025_126427 crossref_primary_10_1016_j_ymssp_2025_112625 crossref_primary_10_1007_s10462_025_11303_w crossref_primary_10_1088_1742_6596_2745_1_012003 crossref_primary_10_1016_j_inffus_2024_102761 crossref_primary_10_1016_j_ress_2025_111004 crossref_primary_10_1109_TCYB_2024_3487934 crossref_primary_10_1016_j_apm_2024_115742 crossref_primary_10_1016_j_ress_2024_110454 crossref_primary_10_1016_j_apm_2024_115906 crossref_primary_10_1016_j_engfailanal_2025_109896 crossref_primary_10_1186_s10033_024_01036_2 crossref_primary_10_1016_j_ymssp_2024_111920 crossref_primary_10_1007_s10845_024_02536_7 crossref_primary_10_1007_s00170_025_15007_x crossref_primary_10_1016_j_inffus_2025_103427 crossref_primary_10_1016_j_ymssp_2024_112293 crossref_primary_10_1016_j_adapen_2025_100223 crossref_primary_10_1109_JSEN_2025_3577708 crossref_primary_10_1016_j_ress_2024_110385 crossref_primary_10_1038_s41598_025_16528_x crossref_primary_10_1007_s10462_024_10820_4 crossref_primary_10_1016_j_jmapro_2024_02_054 crossref_primary_10_1016_j_rser_2023_114224 crossref_primary_10_1007_s12541_025_01343_1 crossref_primary_10_1016_j_jmsy_2025_03_023 crossref_primary_10_1016_j_apenergy_2025_125314 crossref_primary_10_1016_j_ymssp_2025_112683 crossref_primary_10_1007_s12008_025_02364_w crossref_primary_10_59400_sv_v59i1_1685 crossref_primary_10_1016_j_apm_2024_115683 crossref_primary_10_70322_ism_2025_10008 crossref_primary_10_1016_j_ress_2025_110850 crossref_primary_10_3390_machines13040267 crossref_primary_10_1016_j_apm_2024_115844 crossref_primary_10_1016_j_aei_2024_102787 crossref_primary_10_1038_s41598_025_03786_y crossref_primary_10_1016_j_ress_2025_111108 crossref_primary_10_1016_j_ymssp_2024_111420 crossref_primary_10_1016_j_ymssp_2025_113304 crossref_primary_10_1186_s10033_024_01173_8 crossref_primary_10_3390_act14080382 |
| Cites_doi | 10.1016/j.ymssp.2021.108575 10.1016/j.eswa.2020.114316 10.1016/j.ymssp.2018.02.016 10.1016/j.ress.2021.107938 10.1007/s00170-018-3157-5 10.1007/s00163-020-00336-7 10.1109/JSAC.2019.2951964 10.1016/j.asoc.2018.05.015 10.1016/j.jpowsour.2021.230526 10.36001/ijphm.2020.v11i1.2594 10.1016/j.inffus.2019.12.001 10.1016/j.compind.2021.103523 10.1109/TIE.2017.2762639 10.1016/j.jmatprotec.2021.117472 10.1016/j.oceaneng.2022.111299 10.1109/TII.2021.3089333 10.1016/j.renene.2019.06.103 10.1016/j.jcp.2020.109942 10.1016/j.jmsy.2021.03.025 10.1016/j.engappai.2020.103996 10.1016/j.ress.2020.107194 10.1016/j.ymssp.2022.109760 10.1063/1.5031520 10.1016/j.jsv.2021.116196 10.1016/j.tafmec.2019.102447 10.1016/j.engstruct.2021.111882 10.1016/j.tafmec.2020.102872 10.1109/TSMC.2020.3048950 10.1088/1748-9326/abfde9 10.1016/j.ymssp.2022.109772 10.1007/s12289-022-01677-5 10.1016/j.ijfatigue.2021.106352 10.1149/2.0281914jes 10.1016/j.compind.2019.07.004 10.1016/j.jmsy.2020.09.005 10.1007/s10921-020-00705-1 10.1016/j.apenergy.2019.113525 10.33737/jgpps/134845 10.1007/s10846-020-01295-w 10.1002/acs.2784 10.1016/j.compfluid.2018.07.021 10.1016/j.aei.2021.101404 10.1109/ACCESS.2020.2972859 10.1016/j.ress.2022.109067 10.1016/j.ymssp.2021.108709 10.1115/1.4047213 10.1177/1475921720921135 10.1016/j.automatica.2019.108705 10.1016/j.apmt.2020.100898 10.1016/j.neucom.2020.07.088 10.1109/JSEN.2019.2898634 10.1016/j.measurement.2019.107106 10.1016/j.ress.2022.108898 10.1177/1687814016664660 10.1016/j.inffus.2018.10.005 10.1111/mice.12685 10.1016/j.ymssp.2021.108153 10.1016/j.neucom.2021.04.122 10.1016/j.engappai.2020.103678 10.1089/big.2020.0071 10.1016/j.ress.2021.107961 10.26434/chemrxiv.14661180.v1 10.1016/j.ymssp.2021.108779 10.1177/1475921720927488 10.1016/j.compstruc.2020.106458 10.1016/j.engstruct.2020.110704 10.1186/s10033-021-00587-y 10.1016/j.cossms.2019.100797 10.1016/j.ymssp.2017.11.016 10.36001/ijphm.2021.v12i2.2938 10.1016/j.engappai.2021.104295 10.1016/j.cma.2021.113885 10.36001/ijphm.2019.v10i2.2727 10.1109/ICPHM53196.2022.9815692 10.1016/j.engstruct.2020.111582 10.1115/1.4036907 10.1016/j.jmsy.2021.10.013 10.1016/j.jcp.2021.110414 10.1016/j.ymssp.2021.108426 10.3390/app9173473 10.1016/j.cma.2022.114587 10.1016/j.jmsy.2021.11.003 10.1016/j.jcp.2018.10.045 10.1016/j.ymssp.2021.108453 10.1007/s40789-018-0203-8 10.1016/j.ifacol.2021.11.152 10.1016/j.ymssp.2022.108907 10.1016/j.asoc.2020.106665 10.1016/j.jmsy.2019.09.013 10.1016/j.cma.2020.112892 10.1784/insi.2015.57.7.395 10.1016/j.ress.2021.108114 10.1007/s00158-022-03410-x 10.1016/j.jcp.2021.110624 10.24251/HICSS.2019.416 10.1007/s11831-021-09539-0 10.1016/j.ymssp.2021.107614 10.1063/5.0038929 10.1016/j.ymssp.2020.107552 10.3390/s22239494 10.1007/s11831-020-09405-5 10.1016/j.arcontrol.2016.09.008 10.1016/j.ress.2021.108119 10.1016/j.measurement.2019.04.093 10.1109/TPWRS.2020.2988352 10.1504/IJHM.2021.118005 10.3390/en15020558 10.1016/j.jcp.2019.109136 10.1016/j.enggeo.2020.105857 10.1016/j.ijrmms.2020.104568 10.21203/rs.3.rs-863306/v1 10.1016/j.ymssp.2020.107374 10.2514/1.J059250 10.1109/ACCESS.2020.2987324 10.1016/j.ymssp.2017.11.024 10.1016/j.compstruc.2021.106678 10.1016/j.ymssp.2022.108917 10.1016/j.asoc.2016.03.013 10.1109/TBDATA.2015.2465959 10.1016/j.neucom.2020.11.042 10.1002/stc.2358 10.1007/s00158-022-03425-4 10.3390/math9182336 10.1038/s42254-021-00314-5 10.1061/AJRUA6.0001053 10.1016/j.measurement.2021.109631 10.1016/j.measurement.2020.107929 |
| ContentType | Journal Article |
| Copyright | 2023 Elsevier Inc. Distributed under a Creative Commons Attribution 4.0 International License |
| Copyright_xml | – notice: 2023 Elsevier Inc. – notice: Distributed under a Creative Commons Attribution 4.0 International License |
| DBID | AAYXX CITATION 1XC VOOES |
| DOI | 10.1016/j.apm.2023.07.011 |
| DatabaseName | CrossRef Hyper Article en Ligne (HAL) Hyper Article en Ligne (HAL) (Open Access) |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Mathematics |
| EISSN | 1872-8480 |
| EndPage | 352 |
| ExternalDocumentID | oai:HAL:hal-04290849v1 10_1016_j_apm_2023_07_011 S0307904X23003086 |
| GroupedDBID | --K --M -~X .DC .~1 0R~ 1B1 1RT 1~. 1~5 23M 4.4 457 4G. 5GY 5VS 6I. 7-5 71M 8P~ 9JN AACTN AAEDT AAEDW AAFTH AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AAQXK AAXUO ABAOU ABEFU ABFNM ABMAC ABVKL ABXDB ABYKQ ACAZW ACDAQ ACGFS ACNNM ACRLP ADBBV ADEZE ADMUD ADTZH AEBSH AECPX AEKER AENEX AEXQZ AFFNX AFKWA AFTJW AGHFR AGUBO AGYEJ AHHHB AHJVU AIEXJ AIGVJ AIKHN AITUG AJBFU AJOXV ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ ARUGR ASPBG AVWKF AXJTR AZFZN BJAXD BKOJK BLXMC CS3 EBS EFJIC EFLBG EJD EO8 EO9 EP2 EP3 F5P FDB FGOYB FIRID FNPLU FYGXN G-2 G-Q GBLVA HZ~ IHE IXB J1W JJJVA KOM LG9 LY7 M26 M41 MHUIS MO0 MVM N9A NCXOZ O-L O9- OAUVE OK1 OZT P-8 P-9 P2P PC. Q38 R2- RIG ROL RPZ SDF SDG SES SEW SPC SPCBC SST SSW SSZ T5K TN5 WH7 WUQ XJT XPP ZMT ~02 ~G- 9DU AATTM AAXKI AAYWO AAYXX ABJNI ABWVN ACLOT ACRPL ACVFH ADCNI ADNMO ADVLN AEIPS AEUPX AFJKZ AFPUW AGQPQ AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP CITATION EFKBS ~HD 1XC VOOES |
| ID | FETCH-LOGICAL-c374t-ceb15d7b2fa73e768367d57a916ec323ed6dbafd5af4a5b988ca8657f6af45f33 |
| ISICitedReferencesCount | 57 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001061367700001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0307-904X |
| IngestDate | Sat Nov 29 14:59:24 EST 2025 Tue Nov 18 22:40:48 EST 2025 Sat Nov 29 07:21:20 EST 2025 Fri Feb 23 02:35:59 EST 2024 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | ANN CNN DNN GNN NMAE Physics-informed machine learning SVM Knowledge PDE RNN PHM PBM MSE Physics-embedded algorithm structure LSTM NODE RUL RMSE Prognostics and health management PCA FCN MAE ROM Physics-constraint learning CRA KSVD NMSE VAE Physics-informed input space PIML DRM |
| Language | English |
| License | Distributed under a Creative Commons Attribution 4.0 International License: http://creativecommons.org/licenses/by/4.0 |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c374t-ceb15d7b2fa73e768367d57a916ec323ed6dbafd5af4a5b988ca8657f6af45f33 |
| ORCID | 0000-0002-5195-4184 0000-0002-7278-5631 0000-0003-4086-9053 0000-0001-7895-5569 |
| OpenAccessLink | https://hal.science/hal-04290849 |
| PageCount | 28 |
| ParticipantIDs | hal_primary_oai_HAL_hal_04290849v1 crossref_primary_10_1016_j_apm_2023_07_011 crossref_citationtrail_10_1016_j_apm_2023_07_011 elsevier_sciencedirect_doi_10_1016_j_apm_2023_07_011 |
| PublicationCentury | 2000 |
| PublicationDate | December 2023 2023-12-00 2023-12 |
| PublicationDateYYYYMMDD | 2023-12-01 |
| PublicationDate_xml | – month: 12 year: 2023 text: December 2023 |
| PublicationDecade | 2020 |
| PublicationTitle | Applied mathematical modelling |
| PublicationYear | 2023 |
| Publisher | Elsevier Inc Elsevier |
| Publisher_xml | – name: Elsevier Inc – name: Elsevier |
| References | Guc, Chen (bib0169) 2021; 54 Gálvez, Seneviratne, Galar (bib0162) 2021; 4 Jan Hagendorfer (bib0190) 2021 Bills, Sripad, Fredericks, Guttenberg, Charles, Frank, Viswanathan (bib0217) 2020 Tartakovsky, Tipireddy (bib0152) 2019 Zhou, Hong, Jin (bib0045) 2019; 9 Rai, Sahu (bib0072) 2020; 8 Nascimento, Viana (bib0186) 2020; 58 Zheng (bib0044) 2015; 1 Zhang, Gao (bib0191) 2021; 34 Wang, Fan, Kazmer, Gao (bib0206) 2017; 139 Chen, Rao, Feng, Zuo (bib0124) 2022; 171 Sun, Peng, Huang, Li, Long, Wang, Zhao (bib0130) 2021; 18 Hlaing, Morato, Nielsen, Amirafshari, Kolios, Rigo (bib0187) 2022 Raissi, Perdikaris, Karniadakis (bib0139) 2017 P. P.Bonissone, Prognostics & health management at ge Liao, Yang, Wang, Ren (bib0166) 2020; 7 Shukla, Di Leoni, Blackshire, Sparkman, Karniadakis (bib0037) 2020; 39 Dourado, Viana (bib0117) 2020 Karpatne, Watkins, Read, Kumar (bib0094) 2017 Guo, Agarwal, Cooper, Tian, Gao, Grace, Guo (bib0106) 2022; 62 With physics-informed ai, machine operators can trust and verify . von Hahn, Mechefske (bib0123) 2022 Wang, Zheng, Wang, Lu, Jia, Li, Li (bib0214) 2023 Lyathakula, Yuan (bib0055) 2021 Li, Zhao, Sun, Cheng, Chen, Yan, Gao (bib0172) 2021; 52 Goswami, Yin, Yu, Karniadakis (bib0129) 2022; 391 Talukdar, Deka, Doddi, Materassi, Chertkov, Salapaka (bib0105) 2020; 112 Karniadakis, Kevrekidis, Lu, Perdikaris, Wang, Yang (bib0164) 2021; 3 Li, Jia, Feng, Zhu, Miller, Chen, Lee (bib0183) 2020; 151 Xu, Kohtz, Boakye, Gardoni, Wang (bib0029) 2022 Kipchirchir, Do, Njiri, Söffker (bib0049) 2021 Jeong, Yu, Mansour, Vesslinov, Meehan (bib0209) 2020 Viana, Nascimento, Dourado, Yucesan (bib0115) 2021; 245 Ritto, Rochinha (bib0196) 2021; 155 Behjat, Zeng, Rai, Matei, Doermann, Chowdhury (bib0100) 2020; 96 Zhang, Zhang, Wang, Habetler (bib0004) 2020; 8 Stiasny, Misyris, Chatzivasileiadis (bib0140) 2021 Fink, Wang, Svensen, Dersin, Lee, Ducoffe (bib0003) 2020; 92 Wang, Yu (bib0082) 2021 S.K.A.A. Rohit Tripathy, Ilias Bilionis, Physics-informed learning for multiscale systems (pilgrims) Zhang, Rai, Chowdhury, Doermann (bib0047) 2021; 428 Cross, Gibson, Jones, Pitchforth, Zhang, Rogers (bib0135) 2022 Thelen, Zhang, Fink, Lu, Ghosh, Youn, Todd, Mahadevan, Hu, Hu (bib0193) 2023; 66 Star, McKee (bib0178) 2021; 12 Zhang (bib0061) 2012 Xu, Bao, Zhang, Li (bib0215) 2021; 20 Kharazmi, Zhang, Karniadakis (bib0104) 2019 Zhou, Diehl, Tang (bib0171) 2023; 185 Finegan, Zhu, Feng, Keyser, Ulmefors, Li, Bazant, Cooper (bib0071) 2020 Perez-Sanjines, Peeters, Verstraeten, Antoni, Nowé, Helsen (bib0200) 2023; 185 A.I. Ozdagli, X. Koutsoukos, Model-based damage detection through physics-guided learning for dynamic systems. Zamzam, Sidiropoulos (bib0095) 2020; 35 Blasch, Tiley, Sparkman, Donegan, Cherry (bib0101) 2020; volume 11423 Yucesan, Viana, Manin, Mahfoud (bib0107) 2021; 154 Accessed Nov 14, 2019. Odot, Haferssas, Cotin (bib0096) 2021 Lai, Mylonas, Nagarajaiah, Chatzi (bib0156) 2021; 508 Berri, Dalla Vedova, Mainini (bib0016) 2021; 132 Sepasdar, Karpatne, Shakiba (bib0210) 2021 A.D.P. Dourado, F. Viana, Ensemble of hybrid neural networks to compensate for epistemic uncertainties: A case study in system prognosis (2021). Ioannidis, Marques, Giannakis (bib0212) 2019 Neuer (bib0145) 2020 Matei, Zeng, Chowdhury, Rai, de Kleer (bib0102) 2021; 101 Gong, Tang, Yu, Tian (bib0033) 2021 Giorgiani Nascimento, Viana (bib0158) 2019 de Calle, Ferreiro, Arnaiz, Sierra (bib0014) 2019; 112 Cubillo, Perinpanayagam, Esperon-Miguez (bib0056) 2016; 8 Yucesan, Viana (bib0148) 2019; volume 11 Diez-Olivan, Del Ser, Galar, Sierra (bib0040) 2019; 50 Sadoughi, Hu (bib0063) 2019; 19 Arias Chao (bib0010) 2021 Singh, Keyvanlou, Sadhu (bib0005) 2021; 232 Tipireddy, Tartakovsky (bib0149) 2018 Shin, Darbon, Karniadakis (bib0103) 2020 Boushaba, Cauet, Chamroo, Etien, Rambault (bib0165) 2022; 22 Borkowski, Sorini, Chattopadhyay (bib0109) 2022; 258 Pagnier, Chertkov (bib0020) 2021 Guo, Ye, Yang (bib0079) 2020 Baseman, DeBardeleben, Blanchard, Moore, Tkachenko, Ferreira, Siddiqua, Sridharan (bib0154) 2018 R.M. Michael Eidell, S. Choudhry, Pcoe datasets De Groote, Van Hoecke, Crevecoeur (bib0161) 2022; 166 Lei, Li, Guo, Li, Yan, Lin (bib0019) 2018; 104 Kim, An, Choi (bib0001) 2017 Chao, Kulkarni, Goebel, Fink (bib0013) 2019 Sherman, Mellors, Morris (bib0151) 2019 Vesselinov (bib0134) 2019 S. Liu, B.B. Kappes, B. Amin-ahmadi, O. Benafan, X. Zhang, A.P. Stebner, Physics-informed machine learning for composition-process-property alloy design: shape memory alloy demonstration, arXiv preprint arXiv:2003.01878(2020). Nascimento, Fricke, Viana (bib0120) 2020; 96 Shen, Chowdhury, Banerjee, Terejanu (bib0197) 2023 Sadoughi, Hu (bib0064) 2018 Yucesan, Dourado, Viana (bib0018) 2021; 50 Firoozi, Sattarzadeh, Dey (bib0067) 2021 Nabian, Gladstone, Meidani (bib0127) 2021 Karandikar, Schmitz, Smith (bib0076) 2021; 59 Yao, Gao, Liu (bib0091) 2020; 363 Chen, Ma, Yan, Zheng, Zhang (bib0041) 2019 Atamuradov, Medjaher, Dersin, Lamoureux, Zerhouni (bib0031) 2017; 8 Eshkevari, Takáč, Pakzad, Jahani (bib0050) 2021; 229 Kim, Na, Youn (bib0167) 2022; 167 Wang, Li, Zhao, Gao (bib0078) 2020; 57 Deng, Nguyen, Gogu, Morio, Medjaher (bib0181) 2022; volume 7 Muralidhar, Bu, Cao, He, Ramakrishnan, Tafti, Karpatne (bib0073) 2020; 8 Reniers, Mulder, Howey (bib0017) 2019; 166 Khan, Yairi (bib0007) 2018; 107 Tetali, Alguri, Harley (bib0150) 2019 Zhao, Wen, Sun, Sun, Yang, Cao, Dai, Wang (bib0015) 2018; 5 Seo, Meng, Liu (bib0099) 2019 Chakraborty (bib0159) 2021; 426 Nouri, Artozoul, Caillaud, Ammar, Chinesta, Köser (bib0060) 2022; 15 Jia, Willard, Karpatne, Read, Zwart, Steinbach, Kumar (bib0083) 2019 Swischuk, Mainini, Peherstorfer, Willcox (bib0066) 2019; 179 Sun, Peng, Lin, Wang, Zhao, Huang (bib0081) 2021 Zhao, Kang, Tang, Pecht (bib0213) 2017; 65 Rasheed, San, Kvamsdal (bib0011) 2019 Chao, Kulkarni, Goebel, Fink (bib0054) 2022; 217 Kapteyn, Knezevic, Willcox (bib0195) 2020 Pfaff, Fortunato, Sanchez-Gonzalez, Battaglia (bib0069) 2020 Bolandi, Sreekumar, Li, Lajnef, Boddeti (bib0176) 2022 Pawar, San, Vedula, Rasheed, Kvamsdal (bib0039) 2021 Dourado, Viana (bib0121) 2019; volume 11 R. Giorgiani do Nascimento, Hybrid physics-informed neural networks for dynamical systems (2020). Liu, Chen, Du, Tegmark (bib0138) 2021 Chen, Hu, Cheng, Zhang, Zhang (bib0208) 2019; 146 Tidriri, Chatti, Verron, Tiplica (bib0216) 2016; 42 Akrim, Gogu, Guillebot de Nerville, Strahle, Waffa Pagou, Salaun, Vingerhoeds (bib0219) 2022 Liu, Meyendorf, Mrad (bib0048) 2018; volume 1949 Feng, Xu, Han, Li, Incecik (bib0070) 2021; 383 de Silva, Callaham, Jonker, Goebel, Klemisch, McDonald, Hicks, Kutz, Brunton, Aravkin (bib0128) 2020 Goswami, Yin, Yu, Karniadakis (bib0114) 2021 Lizama Molina, Yoon, Jiang, Pyrak-Nolte (bib0153) 2019 Cofre-Martel, Droguett, Modarres (bib0177) 2020 Alizadeh, Allen, Mistree (bib0202) 2020; 31 Singh, Yang, Behjat, Rai, Chowdhury, Matei (bib0038) 2019 Pan, Jing, Wu, Kong (bib0062) 2022; 217 Krishnapriyan, Gholami, Zhe, Kirby, Mahoney (bib0157) 2021 Hou, Wang, Chen, Wang, Peng, Tsui (bib0168) 2022; 169 Goswami, Anitescu, Chakraborty, Rabczuk (bib0147) 2020; 106 Marcus (bib0144) 2021; 1338 Sarih, Tchangani, Medjaher, Péré (bib0194) 2019 Zio (bib0008) 2022; 218 Eker, Camci, Jennions (bib0182) 2019; 10 Yang, Bai, Liu, Liu, Yu (bib0173) 2021; 181 Xu, Noh (bib0199) 2020 Zhang, Zhang, Wang, Zhong (bib0218) 2020 Nor, Pedapait, Muhammad (bib0009) 2021 Liao, Köttig (bib0051) 2016; 44 Green, Langham, Agustin, Quinn, Leeb (bib0201) 2022 Giorgiani do Nascimento, Viana, Corbetta, Kulkarni (bib0179) 2021 Meng, Jing, Yan, Pedrycz (bib0042) 2020; 57 Chen, Gao, Wang, Yan (bib0137) 2021; 16 Yang, Sanchez, Zhang, Roeder, Bowlan, Crochet, Farrar, Mascareñas (bib0088) 2019; 26 M. Sepe, A. Graziano, M. Badora, A. Di Stazio, L. Bellani, M. Compare, E. Zio, A physics-informed machine learning framework for predictive maintenance applied to turbomachinery assets (2021). Hu, Yang, Yan, Xiang, Zhou, Xuan (bib0175) 2020; 142 Rezaeianjouybari, Shang (bib0002) 2020; 163 Daw, Thomas, Carey, Read, Appling, Karpatne (bib0086) 2020 Khan, Hwang, Kim (bib0006) 2021; 9 Scapino, Zondag, Diriken, Rindt, Van Bael, Sciacovelli (bib0012) 2019; 253 Viana, Subramaniyan (bib0125) 2021; 28 Zhang, Sun, Guo (bib0080) 2022; 166 Yucesan, Viana (bib0116) 2020; 11 Wang, Liang, Zheng, Gao, Zhang (bib0036) 2020; 145 Weiderer, Tomé, Lang (bib0057) 2020; 54 Ness, Paul, Sun, Zhang (bib0059) 2022; 302 Freeman, Tang, Huang, VanZwieten (bib0146) 2022; 254 Li, Deka (bib0132) 2021 Rojas, Bitterncourt, Boldrini (bib0133) 2021 Sangid (bib0046) 2020; 24 Yang, Tartakovsky, Tartakovsky (bib0111) 2018 Siddiqui (bib0052) 2017; 8 Jagtap, Kawaguchi, Karniadakis (bib0108) 2020; 404 Mohanty, Vilim (bib0027) 2021 El Mir, Perinpanayagam (bib0110) 2021 von Rueden, Mayer, Beckh, Georgiev, Giesselbach, Heese, Kirsch, Pfrommer, Pick, Ramamurthy (bib0141) 2019 Jiao, Zhao, Lin, Liang (bib0207) 2020; 417 Xu, Noh (bib0112) 2021; 151 Li, Huang, Li, Zhou, Mi (bib0065) 2018; 72 Willard, Jia, Xu, Steinbach, Kumar (bib0032) 2020; 1 Chao, Adey, Fink (bib0160) 2021; 454 Das, Dutta, Putcha, Majumdar, Adak (bib0118) 2020; 6 Ewald, Venkat, Asokkumar, Benedictus, Boller, Groves (bib0174) 2022; 165 Zhang, Liu, Sun (bib0085) 2020; 215 Schröder, Dimitrov, Verelst, Sørensen (bib0126) 2022; 15 W.Z. Levente Klein, Peeking into ai’s ‘black box’ brain – with physics Russell, Wang (bib0131) 2022; 168 Xia, Shao, Williams, Lu, Shu, de Silva (bib0170) 2021; 215 Srikonda, Rastogi, Oestensen (bib0058) 2020 Nguyen, Oterkus, Oterkus (bib0077) 2021; 112 Chakraborty (bib0136) 2020 Chakravarty, Misra, Rai (bib0142) 2021; 137 Chen (bib0035) 2016 Chen, Liu (bib0092) 2021; 168 Peng, Alber, Tepole, Cannon, De, Dura-Bernal, Garikipati, Karniadakis, Lytton, Perdikaris (bib0203) 2021; 28 Thelen, Zhang, Fink, Lu, Ghosh, Youn, Todd, Mahadevan, Hu, Hu (bib0192) 2022; 65 Razak, Cornelio, Cho, Liu, Vaidya, Jafarpour (bib0075) 2021 S. Pepe, J. Liu, E. Quattrocchi, F. Ciucci, Neural ordinary differential equations and recurrent neural networks for predicting the state of health of batteries (2021). Zhao, Zhang, Yu, Sun, Wang, Yan, Chen (bib0030) 2021; 70 Pawar, San, Aksoylu, Rasheed, Kvamsdal (bib0087) 2021; 33 Zheng, Li, Lin, Fink (10.1016/j.apm.2023.07.011_bib0003) 2020; 92 Cofre-Martel (10.1016/j.apm.2023.07.011_bib0177) 2020 Li (10.1016/j.apm.2023.07.011_bib0132) 2021 Vesselinov (10.1016/j.apm.2023.07.011_bib0134) 2019 Freeman (10.1016/j.apm.2023.07.011_bib0146) 2022; 254 Zhou (10.1016/j.apm.2023.07.011_bib0171) 2023; 185 Zhang (10.1016/j.apm.2023.07.011_bib0191) 2021; 34 Shen (10.1016/j.apm.2023.07.011_bib0197) 2023 Sarih (10.1016/j.apm.2023.07.011_bib0194) 2019 10.1016/j.apm.2023.07.011_bib0184 Eshkevari (10.1016/j.apm.2023.07.011_bib0050) 2021; 229 Gong (10.1016/j.apm.2023.07.011_bib0033) 2021 Zhou (10.1016/j.apm.2023.07.011_bib0045) 2019; 9 Shukla (10.1016/j.apm.2023.07.011_bib0037) 2020; 39 Zhang (10.1016/j.apm.2023.07.011_bib0047) 2021; 428 Xu (10.1016/j.apm.2023.07.011_bib0112) 2021; 151 de Silva (10.1016/j.apm.2023.07.011_bib0128) 2020 Zheng (10.1016/j.apm.2023.07.011_bib0044) 2015; 1 Pakravan (10.1016/j.apm.2023.07.011_bib0098) 2021; 440 Reniers (10.1016/j.apm.2023.07.011_bib0017) 2019; 166 de Calle (10.1016/j.apm.2023.07.011_bib0014) 2019; 112 Lizama Molina (10.1016/j.apm.2023.07.011_bib0153) 2019 Matei (10.1016/j.apm.2023.07.011_bib0102) 2021; 101 Yucesan (10.1016/j.apm.2023.07.011_bib0018) 2021; 50 Jeong (10.1016/j.apm.2023.07.011_bib0209) 2020 Zhang (10.1016/j.apm.2023.07.011_bib0085) 2020; 215 Giorgiani Nascimento (10.1016/j.apm.2023.07.011_bib0158) 2019 Zhang (10.1016/j.apm.2023.07.011_bib0084) 2021; 20 Deng (10.1016/j.apm.2023.07.011_bib0181) 2022; volume 7 Peng (10.1016/j.apm.2023.07.011_bib0203) 2021; 28 Sepasdar (10.1016/j.apm.2023.07.011_bib0210) 2021 von Rueden (10.1016/j.apm.2023.07.011_bib0141) 2019 Behjat (10.1016/j.apm.2023.07.011_bib0100) 2020; 96 Nascimento (10.1016/j.apm.2023.07.011_bib0186) 2020; 58 Zhang (10.1016/j.apm.2023.07.011_bib0061) 2012 Yang (10.1016/j.apm.2023.07.011_bib0088) 2019; 26 10.1016/j.apm.2023.07.011_bib0180 Russell (10.1016/j.apm.2023.07.011_bib0131) 2022; 168 Yucesan (10.1016/j.apm.2023.07.011_bib0107) 2021; 154 Yu (10.1016/j.apm.2023.07.011_bib0068) 2018 Ioannidis (10.1016/j.apm.2023.07.011_bib0212) 2019 Chao (10.1016/j.apm.2023.07.011_bib0013) 2019 Zhao (10.1016/j.apm.2023.07.011_bib0213) 2017; 65 Sadoughi (10.1016/j.apm.2023.07.011_bib0063) 2019; 19 Nguyen (10.1016/j.apm.2023.07.011_bib0077) 2021; 112 Zhao (10.1016/j.apm.2023.07.011_bib0015) 2018; 5 Pagnier (10.1016/j.apm.2023.07.011_bib0020) 2021 Jan Hagendorfer (10.1016/j.apm.2023.07.011_bib0190) 2021 Cubillo (10.1016/j.apm.2023.07.011_bib0056) 2016; 8 Eker (10.1016/j.apm.2023.07.011_bib0182) 2019; 10 Weiderer (10.1016/j.apm.2023.07.011_bib0057) 2020; 54 Wang (10.1016/j.apm.2023.07.011_bib0214) 2023 Liao (10.1016/j.apm.2023.07.011_bib0166) 2020; 7 Firoozi (10.1016/j.apm.2023.07.011_bib0067) 2021 Star (10.1016/j.apm.2023.07.011_bib0178) 2021; 12 Zhang (10.1016/j.apm.2023.07.011_bib0080) 2022; 166 Goswami (10.1016/j.apm.2023.07.011_bib0129) 2022; 391 Chen (10.1016/j.apm.2023.07.011_bib0208) 2019; 146 Zhao (10.1016/j.apm.2023.07.011_bib0030) 2021; 70 Razak (10.1016/j.apm.2023.07.011_bib0075) 2021 Tartakovsky (10.1016/j.apm.2023.07.011_bib0152) 2019 Xia (10.1016/j.apm.2023.07.011_bib0170) 2021; 215 Hanachi (10.1016/j.apm.2023.07.011_bib0043) 2019; 101 Yang (10.1016/j.apm.2023.07.011_bib0111) 2018 Wang (10.1016/j.apm.2023.07.011_bib0082) 2021 Chen (10.1016/j.apm.2023.07.011_bib0035) 2016 Alizadeh (10.1016/j.apm.2023.07.011_bib0202) 2020; 31 Berri (10.1016/j.apm.2023.07.011_bib0016) 2021; 132 De Groote (10.1016/j.apm.2023.07.011_bib0161) 2022; 166 Finegan (10.1016/j.apm.2023.07.011_bib0071) 2020 Hlaing (10.1016/j.apm.2023.07.011_bib0187) 2022 Kim (10.1016/j.apm.2023.07.011_bib0001) 2017 Jia (10.1016/j.apm.2023.07.011_bib0083) 2019 Dourado (10.1016/j.apm.2023.07.011_bib0121) 2019; volume 11 Pawar (10.1016/j.apm.2023.07.011_bib0039) 2021 Yang (10.1016/j.apm.2023.07.011_bib0173) 2021; 181 Thelen (10.1016/j.apm.2023.07.011_bib0192) 2022; 65 10.1016/j.apm.2023.07.011_bib0089 Kharazmi (10.1016/j.apm.2023.07.011_bib0104) 2019 Guo (10.1016/j.apm.2023.07.011_bib0106) 2022; 62 Bills (10.1016/j.apm.2023.07.011_bib0217) 2020 Lai (10.1016/j.apm.2023.07.011_bib0156) 2021; 508 Rai (10.1016/j.apm.2023.07.011_bib0072) 2020; 8 Cross (10.1016/j.apm.2023.07.011_bib0135) 2022 Yucesan (10.1016/j.apm.2023.07.011_bib0116) 2020; 11 Marcus (10.1016/j.apm.2023.07.011_bib0144) 2021; 1338 Sun (10.1016/j.apm.2023.07.011_bib0130) 2021; 18 Chen (10.1016/j.apm.2023.07.011_bib0041) 2019 Gao (10.1016/j.apm.2023.07.011_bib0053) 2020 Chen (10.1016/j.apm.2023.07.011_bib0137) 2021; 16 Daw (10.1016/j.apm.2023.07.011_bib0086) 2020 Hu (10.1016/j.apm.2023.07.011_bib0175) 2020; 142 10.1016/j.apm.2023.07.011_bib0097 Akrim (10.1016/j.apm.2023.07.011_bib0219) 2022 Tetali (10.1016/j.apm.2023.07.011_bib0150) 2019 Odot (10.1016/j.apm.2023.07.011_bib0096) 2021 Singh (10.1016/j.apm.2023.07.011_bib0038) 2019 Chao (10.1016/j.apm.2023.07.011_bib0160) 2021; 454 Nouri (10.1016/j.apm.2023.07.011_bib0060) 2022; 15 Geng (10.1016/j.apm.2023.07.011_bib0074) 2020; 279 Zio (10.1016/j.apm.2023.07.011_bib0008) 2022; 218 Zhang (10.1016/j.apm.2023.07.011_bib0004) 2020; 8 Ewald (10.1016/j.apm.2023.07.011_bib0174) 2022; 165 Sun (10.1016/j.apm.2023.07.011_bib0081) 2021 Shen (10.1016/j.apm.2023.07.011_bib0143) 2021; 103 Swischuk (10.1016/j.apm.2023.07.011_bib0066) 2019; 179 Dourado (10.1016/j.apm.2023.07.011_bib0117) 2020 Chen (10.1016/j.apm.2023.07.011_bib0092) 2021; 168 El Mir (10.1016/j.apm.2023.07.011_bib0110) 2021 Neuer (10.1016/j.apm.2023.07.011_bib0145) 2020 Xu (10.1016/j.apm.2023.07.011_bib0199) 2020 Pan (10.1016/j.apm.2023.07.011_bib0062) 2022; 217 Gálvez (10.1016/j.apm.2023.07.011_bib0162) 2021; 4 Mohanty (10.1016/j.apm.2023.07.011_bib0027) 2021 Stiasny (10.1016/j.apm.2023.07.011_bib0140) 2021 Ness (10.1016/j.apm.2023.07.011_bib0059) 2022; 302 Liao (10.1016/j.apm.2023.07.011_bib0051) 2016; 44 Karandikar (10.1016/j.apm.2023.07.011_bib0076) 2021; 59 Pawar (10.1016/j.apm.2023.07.011_bib0087) 2021; 33 10.1016/j.apm.2023.07.011_bib0119 Sangid (10.1016/j.apm.2023.07.011_bib0046) 2020; 24 Jagtap (10.1016/j.apm.2023.07.011_bib0108) 2020; 404 Lyathakula (10.1016/j.apm.2023.07.011_bib0055) 2021 Zhang (10.1016/j.apm.2023.07.011_bib0218) 2020 Liu (10.1016/j.apm.2023.07.011_bib0204) 2018; 108 Siddiqui (10.1016/j.apm.2023.07.011_bib0052) 2017; 8 Ma (10.1016/j.apm.2023.07.011_bib0188) 2023; 229 Rezaeianjouybari (10.1016/j.apm.2023.07.011_bib0002) 2020; 163 Srikonda (10.1016/j.apm.2023.07.011_bib0058) 2020 Chen (10.1016/j.apm.2023.07.011_bib0211) 2019; 38 von Hahn (10.1016/j.apm.2023.07.011_bib0123) 2022 Chakraborty (10.1016/j.apm.2023.07.011_bib0136) 2020 Guo (10.1016/j.apm.2023.07.011_bib0079) 2020 Nabian (10.1016/j.apm.2023.07.011_bib0127) 2021 Goswami (10.1016/j.apm.2023.07.011_bib0147) 2020; 106 Yao (10.1016/j.apm.2023.07.011_bib0091) 2020; 363 Li (10.1016/j.apm.2023.07.011_bib0172) 2021; 52 Krishnapriyan (10.1016/j.apm.2023.07.011_bib0157) 2021 Xu (10.1016/j.apm.2023.07.011_bib0215) 2021; 20 Scapino (10.1016/j.apm.2023.07.011_bib0012) 2019; 253 Sherman (10.1016/j.apm.2023.07.011_bib0151) 2019 Jiao (10.1016/j.apm.2023.07.011_bib0207) 2020; 417 Willard (10.1016/j.apm.2023.07.011_bib0032) 2020; 1 Talukdar (10.1016/j.apm.2023.07.011_bib0105) 2020; 112 Zheng (10.1016/j.apm.2023.07.011_bib0163) 2021 Perez-Sanjines (10.1016/j.apm.2023.07.011_bib0200) 2023; 185 Chao (10.1016/j.apm.2023.07.011_bib0054) 2022; 217 Viana (10.1016/j.apm.2023.07.011_bib0125) 2021; 28 Guc (10.1016/j.apm.2023.07.011_bib0169) 2021; 54 Yucesan (10.1016/j.apm.2023.07.011_bib0148) 2019; volume 11 Arias Chao (10.1016/j.apm.2023.07.011_bib0010) 2021 Green (10.1016/j.apm.2023.07.011_bib0201) 2022 Diez-Olivan (10.1016/j.apm.2023.07.011_bib0040) 2019; 50 Li (10.1016/j.apm.2023.07.011_bib0065) 2018; 72 Lei (10.1016/j.apm.2023.07.011_bib0019) 2018; 104 Pfingstl (10.1016/j.apm.2023.07.011_bib0185) 2022; 171 Karniadakis (10.1016/j.apm.2023.07.011_bib0164) 2021; 3 10.1016/j.apm.2023.07.011_bib0023 10.1016/j.apm.2023.07.011_bib0022 Seo (10.1016/j.apm.2023.07.011_bib0099) 2019 Rasheed (10.1016/j.apm.2023.07.011_bib0011) 2019 10.1016/j.apm.2023.07.011_bib0025 10.1016/j.apm.2023.07.011_bib0024 Das (10.1016/j.apm.2023.07.011_bib0118) 2020; 6 Nascimento (10.1016/j.apm.2023.07.011_bib0120) 2020; 96 Chakraborty (10.1016/j.apm.2023.07.011_bib0159) 2021; 426 Kapteyn (10.1016/j.apm.2023.07.011_bib0195) 2020 Goswami (10.1016/j.apm.2023.07.011_bib0114) 2021 10.1016/j.apm.2023.07.011_bib0026 Borkowski (10.1016/j.apm.2023.07.011_bib0109) 2022; 258 10.1016/j.apm.2023.07.011_bib0028 Ritto (10.1016/j.apm.2023.07.011_bib0196) 2021; 155 Kim (10.1016/j.apm.2023.07.011_bib0189) 2021 Kim (10.1016/j.apm.2023.07.011_bib0167) 2022; 167 Kipchirchir (10.1016/j.apm.2023.07.011_bib0049) 2021 Xu (10.1016/j.apm.2023.07.011_bib0029) 2022 Li (10.1016/j.apm.2023.07.011_bib0034) 2022; 62 10.1016/j.apm.2023.07.011_bib0155 Pfaff (10.1016/j.apm.2023.07.011_bib0069) 2020 Blasch (10.1016/j.apm.2023.07.011_bib0101) 2020; volume 11423 Sadoughi (10.1016/j.apm.2023.07.011_bib0064) 2018 Chakravarty (10.1016/j.apm.2023.07.011_bib0142) 2021; 137 Chen (10.1016/j.apm.2023.07.011_bib0090) 2021; 445 Shin (10.1016/j.apm.2023.07.011_bib0103) 2020 Karpatne (10.1016/j.apm.2023.07.011_bib0094) 2017 Zamzam (10.1016/j.apm.2023.07.011_bib0095) 2020; 35 Tidriri (10.1016/j.apm.2023.07.011_bib0216) 2016; 42 Wang (10.1016/j.apm.2023.07.011_bib0078) 2020; 57 Baseman (10.1016/j.apm.2023.07.011_bib0154) 2018 Wang (10.1016/j.apm.2023.07.011_bib0206) 2017; 139 Viana (10.1016/j.apm.2023.07.011_bib0115) 2021; 245 Khan (10.1016/j.apm.2023.07.011_bib0006) 2021; 9 Wang (10.1016/j.apm.2023.07.011_bib0036) 2020; 145 Liu (10.1016/j.apm.2023.07.011_bib0138) 2021 Giorgiani do Nascimento (10.1016/j.apm.2023.07.011_bib0179) 2021 Raissi (10.1016/j.apm.2023.07.011_bib0021) 2019; 378 Liu (10.1016/j.apm.2023.07.011_bib0048) 2018; volume 1949 10.1016/j.apm.2023.07.011_bib0122 Chen (10.1016/j.apm.2023.07.011_bib0124) 2022; 171 Tipireddy (10.1016/j.apm.2023.07.011_bib0149) 2018 Meng (10.1016/j.apm.2023.07.011_bib0042) 2020; 57 Li (10.1016/j.apm.2023.07.011_bib0183) 2020; 151 Bolandi (10.1016/j.apm.2023.07.011_bib0176) 2022 Leturiondo (10.1016/j.apm.2023.07.011_bib0198) 2015; 57 Atamuradov (10.1016/j.apm.2023.07.011_bib0031) 2017; 8 |
| References_xml | – volume: 454 start-page: 324 year: 2021 end-page: 338 ident: bib0160 article-title: Implicit supervision for fault detection and segmentation of emerging fault types with deep variational autoencoders publication-title: Neurocomputing – start-page: arXiv year: 2019 end-page: 1901 ident: bib0158 article-title: Fleet prognosis with physics-informed recurrent neural networks publication-title: arXiv e-prints – volume: 108 start-page: 33 year: 2018 end-page: 47 ident: bib0204 article-title: Artificial intelligence for fault diagnosis of rotating machinery: a review publication-title: Mech. Syst. Signal Process. – reference: ). – volume: 179 start-page: 704 year: 2019 end-page: 717 ident: bib0066 article-title: Projection-based model reduction: formulations for physics-based machine learning publication-title: Comput. Fluids – year: 2021 ident: bib0067 article-title: Cylindrical battery fault detection under extreme fast charging: a physics-based learning approach publication-title: arXiv preprint arXiv:2105.02169 – volume: 96 start-page: 106665 year: 2020 ident: bib0100 article-title: A physics-aware learning architecture with input transfer networks for predictive modeling publication-title: Appl. Soft. Comput. – volume: 165 start-page: 108153 year: 2022 ident: bib0174 article-title: Perception modelling by invariant representation of deep learning for automated structural diagnostic in aircraft maintenance: astudy case using deepshm publication-title: Mech. Syst. Signal Process. – volume: 155 start-page: 107614 year: 2021 ident: bib0196 article-title: Digital twin, physics-based model, and machine learning applied to damage detection in structures publication-title: Mech. Syst. Signal Process. – volume: 57 start-page: 115 year: 2020 end-page: 129 ident: bib0042 article-title: A survey on machine learning for data fusion publication-title: Inf. Fusion – volume: 166 start-page: 108426 year: 2022 ident: bib0080 article-title: Transfer-learning guided bayesian model updating for damage identification considering modeling uncertainty publication-title: Mech. Syst. Signal Process. – volume: 54 start-page: 53 year: 2021 end-page: 58 ident: bib0169 article-title: Fault cause assignment with physics informed transfer learning publication-title: IFAC-PapersOnLine – volume: 1 start-page: 16 year: 2015 end-page: 34 ident: bib0044 article-title: Methodologies for cross-domain data fusion: an overview publication-title: IEEE Trans. Big Data – volume: 42 start-page: 63 year: 2016 end-page: 81 ident: bib0216 article-title: Bridging data-driven and model-based approaches for process fault diagnosis and health monitoring: a review of researches and future challenges publication-title: Annu. Rev. Control – year: 2022 ident: bib0219 article-title: A framework for generating large data sets for fatigue damage prognostic problems publication-title: Proc. of 2022 IEEE International Conference on Prognostics and Health Management (ICPHM) – volume: 20 start-page: 1675 year: 2021 end-page: 1688 ident: bib0084 article-title: Structural damage identification via physics-guided machine learning: a methodology integrating pattern recognition with finite element model updating publication-title: Struct. Health Monitor. – volume: 3 start-page: 422 year: 2021 end-page: 440 ident: bib0164 article-title: Physics-informed machine learning publication-title: Nature Rev. Phys. – volume: 132 start-page: 103523 year: 2021 ident: bib0016 article-title: Computational framework for real-time diagnostics and prognostics of aircraft actuation systems publication-title: Comput. Ind. – volume: 245 start-page: 106458 year: 2021 ident: bib0115 article-title: Estimating model inadequacy in ordinary differential equations with physics-informed neural networks publication-title: Comput. Struct. – year: 2017 ident: bib0139 article-title: Physics informed deep learning (part i): data-driven solutions of nonlinear partial differential equations publication-title: arXiv preprint arXiv:1711.10561 – volume: 104 start-page: 799 year: 2018 end-page: 834 ident: bib0019 article-title: Machinery health prognostics: a systematic review from data acquisition to rul prediction publication-title: Mech. Syst. Signal Process. – volume: 8 start-page: 71050 year: 2020 end-page: 71073 ident: bib0072 article-title: Driven by data or derived through physics? a review of hybrid physics guided machine learning techniques with cyber-physical system (cps) focus publication-title: IEEE Access – year: 2020 ident: bib0177 article-title: Uncovering the underlying physics of degrading system behavior through a deep neural network framework: the case of remaining useful life prognosis publication-title: arXiv preprint arXiv:2006.09288 – volume: 19 start-page: 4181 year: 2019 end-page: 4192 ident: bib0063 article-title: Physics-based convolutional neural network for fault diagnosis of rolling element bearings publication-title: IEEE Sens. J. – reference: R.M. Michael Eidell, S. Choudhry, Pcoe datasets, ( – start-page: arXiv year: 2021 end-page: 2107 ident: bib0132 article-title: Physics-informed graph learning for robust fault location in distribution systems publication-title: arXiv e-prints – volume: 106 start-page: 102447 year: 2020 ident: bib0147 article-title: Transfer learning enhanced physics informed neural network for phase-field modeling of fracture publication-title: Theor. Appl. Fract. Mech. – reference: S.K.A.A. Rohit Tripathy, Ilias Bilionis, Physics-informed learning for multiscale systems (pilgrims), ( – year: 2021 ident: bib0163 article-title: Physics-guided machine learning approach to characterizing small-scale fractures in geothermal fields publication-title: Proceedings, forty-sixth workshop on geothermal reservoir engineering, Stanford University – volume: 163 start-page: 107929 year: 2020 ident: bib0002 article-title: Deep learning for prognostics and health management: state of the art, challenges, and opportunities publication-title: Measurement – reference: R.M. Michael Eidell, S. Choudhry, Accelerating product development with physics-informed neural networks and nvidia modulus, ( – volume: 217 start-page: 107961 year: 2022 ident: bib0054 article-title: Fusing physics-based and deep learning models for prognostics publication-title: Reliab. Eng. Syst. Saf. – start-page: 5919 year: 2018 end-page: 5923 ident: bib0064 article-title: A physics-based deep learning approach for fault diagnosis of rotating machinery publication-title: IECON 2018-44th Annual Conference of the IEEE Industrial Electronics Society – volume: 426 start-page: 109942 year: 2021 ident: bib0159 article-title: Transfer learning based multi-fidelity physics informed deep neural network publication-title: J. Comput. Phys. – year: 2021 ident: bib0138 article-title: Physics-augmented learning: a new paradigm beyond physics-informed learning publication-title: arXiv preprint arXiv:2109.13901 – volume: 166 start-page: 108453 year: 2022 ident: bib0161 article-title: Prediction of follower jumps in cam-follower mechanisms: the benefit of using physics-inspired features in recurrent neural networks publication-title: Mech. Syst. Signal Process. – volume: volume 7 start-page: 118 year: 2022 end-page: 125 ident: bib0181 article-title: Physics-informed lightweight temporal convolution networks for fault prognostics associated to bearing stiffness degradation publication-title: PHM Society European Conference – volume: 92 start-page: 103678 year: 2020 ident: bib0003 article-title: Potential, challenges and future directions for deep learning in prognostics and health management applications publication-title: Eng. Appl. Artif. Intell. – start-page: 8157 year: 2019 end-page: 8161 ident: bib0212 article-title: A recurrent graph neural network for multi-relational data publication-title: ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) – volume: volume 11 year: 2019 ident: bib0148 article-title: Wind turbine main bearing fatigue life estimation with physicsinformed neural networks publication-title: Annual Conference of the PHM Society – volume: 5 start-page: 167 year: 2018 end-page: 178 ident: bib0015 article-title: Similarity criteria and coal-like material in coal and gas outburst physical simulation publication-title: Int. J. Coal Sci. Technol. – year: 2021 ident: bib0027 article-title: Physics-Infused AI/ML Based Digital-Twin Framework for Flow-Induced-Vibration Damage Prediction in a Nuclear Reactor Heat Exchanger publication-title: Technical Report – year: 2019 ident: bib0151 article-title: Subsurface monitoring via physics-informed deep neural network analysis of das publication-title: 53rd US Rock Mechanics/Geomechanics Symposium – volume: 35 start-page: 4347 year: 2020 end-page: 4356 ident: bib0095 article-title: Physics-aware neural networks for distribution system state estimation publication-title: IEEE Trans. Power Syst. – year: 2021 ident: bib0010 publication-title: Combining Deep Learning and Physics-Based Performance Models for Diagnostics and Prognostics – reference: ). Accessed Nov 14, 2019. – reference: S. Pepe, J. Liu, E. Quattrocchi, F. Ciucci, Neural ordinary differential equations and recurrent neural networks for predicting the state of health of batteries (2021). – volume: 145 start-page: 642 year: 2020 end-page: 650 ident: bib0036 article-title: An integrated fault diagnosis and prognosis approach for predictive maintenance of wind turbine bearing with limited samples publication-title: Renew. Energy – volume: 33 start-page: 011701 year: 2021 ident: bib0087 article-title: Physics guided machine learning using simplified theories publication-title: Phys. Fluids – year: 2022 ident: bib0201 article-title: Physics-informed feature space evaluation for diagnostic power monitoring publication-title: IEEE Trans. Ind. Inf. – start-page: 106352 year: 2021 ident: bib0055 article-title: A probabilistic fatigue life prediction for adhesively bonded joints via anns-based hybrid model publication-title: Int. J. Fatigue – volume: 8 start-page: 29857 year: 2020 end-page: 29881 ident: bib0004 article-title: Deep learning algorithms for bearing fault diagnostics–a comprehensive review publication-title: IEEE Access – start-page: 1 year: 2021 end-page: 6 ident: bib0110 article-title: Certification approach for physics informed machine learning and its application in landing gear life assessment publication-title: 2021 IEEE/AIAA 40th Digital Avionics Systems Conference (DASC) – start-page: 1 year: 2019 end-page: 5 ident: bib0041 article-title: A permutation entropy-based importance measure for condition monitoring data fusion in fault diagnosis publication-title: 2019 Prognostics and System Health Management Conference (PHM-Qingdao) – volume: 103 start-page: 104295 year: 2021 ident: bib0143 article-title: A physics-informed deep learning approach for bearing fault detection publication-title: Eng. Appl. Artif. Intell. – volume: 154 start-page: 107552 year: 2021 ident: bib0107 article-title: Adjusting a torsional vibration damper model with physics-informed neural networks publication-title: Mech. Syst. Signal Process. – volume: 215 start-page: 110704 year: 2020 ident: bib0085 article-title: Physics-guided convolutional neural network (phycnn) for data-driven seismic response modeling publication-title: Eng. Struct. – volume: 218 start-page: 108119 year: 2022 ident: bib0008 article-title: Prognostics and health management (phm): where are we and where do we (need to) go in theory and practice publication-title: Reliab. Eng. Syst. Saf. – year: 2012 ident: bib0061 article-title: A knowledge-based reasoning model using causal table for identifying corrosion failure mechanisms in refining and petrochemical plants publication-title: Eng. Fail Anal. – start-page: 347 year: 2022 end-page: 367 ident: bib0135 article-title: Physics-informed Machine Learning for Structural Health Monitoring publication-title: Structural Health Monitoring Based on Data Science Techniques – year: 2020 ident: bib0136 article-title: Simulation free reliability analysis: aphysics-informed deep learning based approach publication-title: arXiv preprint arXiv:2005.01302 – reference: M. Sepe, A. Graziano, M. Badora, A. Di Stazio, L. Bellani, M. Compare, E. Zio, A physics-informed machine learning framework for predictive maintenance applied to turbomachinery assets (2021). – volume: 101 start-page: 2861 year: 2019 end-page: 2872 ident: bib0043 article-title: Hybrid data-driven physics-based model fusion framework for tool wear prediction publication-title: Int. J. Adv. Manuf. Technol. – year: 2016 ident: bib0035 article-title: Citespace: A practical guide for mapping scientific literature – start-page: 532 year: 2020 end-page: 540 ident: bib0086 article-title: Physics-guided architecture (pga) of neural networks for quantifying uncertainty in lake temperature modeling publication-title: Proceedings of the 2020 siam international conference on data mining – volume: 4 start-page: 230 year: 2021 end-page: 258 ident: bib0162 article-title: Development and synchronisation of a physics-based model for heating, ventilation and air conditioning system integrated into a hybrid model publication-title: Int. J. Hydromechatron. – year: 2021 ident: bib0127 article-title: Efficient training of physics-informed neural networks via importance sampling publication-title: Comput.-Aided Civ. Infrastruct. Eng. – volume: 50 start-page: 92 year: 2019 end-page: 111 ident: bib0040 article-title: Data fusion and machine learning for industrial prognosis: trends and perspectives towards industry 4.0 publication-title: Inf. Fusion – volume: 7 start-page: 241 year: 2020 end-page: 249 ident: bib0166 article-title: Fault diagnosis of power transformers using graph convolutional network publication-title: CSEE J. Power Energy Syst. – year: 2017 ident: bib0001 article-title: Prognostics and health management of engineering systems – volume: 9 start-page: 2336 year: 2021 ident: bib0006 article-title: Synthetic data augmentation and deep learning for the fault diagnosis of rotating machines publication-title: Mathematics – volume: 16 start-page: 065003 year: 2021 ident: bib0137 article-title: Physics-informed generative neural network: an application to troposphere temperature prediction publication-title: Environ. Res. Lett. – year: 2019 ident: bib0153 article-title: Acoustic emission analysis of fracture initiation and propagation using physics-informed machine learning methods publication-title: Technical Report – volume: 391 start-page: 114587 year: 2022 ident: bib0129 article-title: A physics-informed variational deeponet for predicting crack path in quasi-brittle materials publication-title: Comput. Methods Appl. Mech. Eng. – start-page: 230 year: 2021 end-page: 240 ident: bib0190 article-title: Knowledge incorporation for machine learning in condition monitoring: A survey publication-title: 2021 International Symposium on Electrical, Electronics and Information Engineering – volume: 28 start-page: 1017 year: 2021 end-page: 1037 ident: bib0203 article-title: Multiscale modeling meets machine learning: what can we learn? publication-title: Arch. Comput. Methods Eng. – year: 2019 ident: bib0134 article-title: Unsupervised and Physics-Informed Machine Learning of Big and Noisy Data publication-title: Technical Report – volume: 39 start-page: 101876 year: 2021 ident: bib0093 article-title: Fatigue property prediction of additively manufactured ti-6al-4v using probabilistic physics-guided learning publication-title: Addit. Manuf. – year: 2018 ident: bib0149 article-title: Physics-informed machine learning method for forecasting and uncertainty quantification of partially observed and unobserved states in power grids publication-title: arXiv preprint arXiv:1806.10990 – year: 2021 ident: bib0096 article-title: Deepphysics: a physics aware deep learning framework for real-time simulation publication-title: arXiv preprint arXiv:2109.09491 – volume: 72 start-page: 624 year: 2018 end-page: 635 ident: bib0065 article-title: Physics of failure-based reliability prediction of turbine blades using multi-source information fusion publication-title: Appl. Soft. Comput. – year: 2020 ident: bib0218 article-title: Dynet: dynamic convolution for accelerating convolutional neural networks publication-title: arXiv preprint arXiv:2004.10694 – volume: 22 start-page: 9494 year: 2022 ident: bib0165 article-title: Comparative study between physics-informed cnn and pca in induction motor broken bars mcsa detection publication-title: Sensors – start-page: 1 year: 2018 end-page: 6 ident: bib0154 article-title: Physics-informed machine learning for dram error modeling publication-title: 2018 IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems (DFT) – volume: 62 start-page: 145 year: 2022 end-page: 163 ident: bib0106 article-title: Machine learning for metal additive manufacturing: towards a physics-informed data-driven paradigm publication-title: J. Manuf. Syst. – volume: 302 start-page: 117472 year: 2022 ident: bib0059 article-title: Towards a generic physics-based machine learning model for geometry invariant thermal history prediction in additive manufacturing publication-title: J. Mater. Process. Technol. – volume: 171 start-page: 108917 year: 2022 ident: bib0185 article-title: On integrating prior knowledge into gaussian processes for prognostic health monitoring publication-title: Mech. Syst. Signal Process. – volume: volume 1949 start-page: 020023 year: 2018 ident: bib0048 article-title: The role of data fusion in predictive maintenance using digital twin publication-title: AIP Conference Proceedings – start-page: 1 year: 2022 end-page: 19 ident: bib0187 article-title: Inspection and maintenance planning for offshore wind structural components: integrating fatigue failure criteria with bayesian networks and markov decision processes publication-title: Struct. Infrastruct. Eng. – volume: 171 start-page: 108907 year: 2022 ident: bib0124 article-title: Physics-informed lstm hyperparameters selection for gearbox fault detection publication-title: Mech. Syst. Signal Process. – volume: 15 start-page: 558 year: 2022 ident: bib0126 article-title: Using transfer learning to build physics-informed machine learning models for improved wind farm monitoring publication-title: Energies – volume: 279 start-page: 105857 year: 2020 ident: bib0074 article-title: Physics-guided deep learning for predicting geological drilling risk of wellbore instability using seismic attributes data publication-title: Eng. Geol. – volume: 50 start-page: 101404 year: 2021 ident: bib0018 article-title: A survey of modeling for prognosis and health management of industrial equipment publication-title: Adv. Eng. Inf. – volume: 168 start-page: 108709 year: 2022 ident: bib0131 article-title: Physics-informed deep learning for signal compression and reconstruction of big data in industrial condition monitoring publication-title: Mech. Syst. Signal Process. – year: 2019 ident: bib0141 article-title: Informed machine learning–a taxonomy and survey of integrating knowledge into learning systems publication-title: arXiv preprint arXiv:1903.12394 – reference: S. Liu, B.B. Kappes, B. Amin-ahmadi, O. Benafan, X. Zhang, A.P. Stebner, Physics-informed machine learning for composition-process-property alloy design: shape memory alloy demonstration, arXiv preprint arXiv:2003.01878(2020). – volume: 39 start-page: 1 year: 2020 end-page: 20 ident: bib0037 article-title: Physics-informed neural network for ultrasound nondestructive quantification of surface breaking cracks publication-title: J. Nondestr. Eval. – start-page: 109067 year: 2023 ident: bib0214 article-title: Sensor fault detection of vehicle suspension systems based on transmissibility operators and neyman–pearson test publication-title: Reliab. Eng. Syst. Saf. – volume: 112 start-page: 103114 year: 2019 ident: bib0014 article-title: Dynamic condition monitoring method based on dimensionality reduction techniques for data-limited industrial environments publication-title: Comput. Ind. – year: 2021 ident: bib0081 article-title: Microcrack defect quantification using a focusing high-order sh guided wave emat: the physics-informed deep neural network guwnet publication-title: IEEE Trans. Ind. Inf. – year: 2021 ident: bib0210 article-title: A data-driven approach to full-field damage and failure pattern prediction in microstructure-dependent composites using deep learning publication-title: arXiv preprint arXiv:2104.04485 – volume: 8 start-page: 1 year: 2017 end-page: 31 ident: bib0031 article-title: Prognostics and health management for maintenance practitioners-review, implementation and tools evaluation publication-title: Int. J. Prognost. Health Manag. – start-page: 558 year: 2019 end-page: 566 ident: bib0083 article-title: Physics guided rnns for modeling dynamical systems: A case study in simulating lake temperature profiles publication-title: Proceedings of the 2019 SIAM International Conference on Data Mining – volume: 142 year: 2020 ident: bib0175 article-title: Damage localization in pressure vessel by guided waves based on convolution neural network approach publication-title: J. Press. Vessel Technol. – volume: 151 start-page: 107106 year: 2020 ident: bib0183 article-title: A novel scalable method for machine degradation assessment using deep convolutional neural network publication-title: Measurement – volume: 20 start-page: 1494 year: 2021 end-page: 1517 ident: bib0215 article-title: Attribute-based structural damage identification by few-shot meta learning with inter-class knowledge transfer publication-title: Struct. Health Monitor. – volume: 57 start-page: 298 year: 2020 end-page: 310 ident: bib0078 article-title: Physics guided neural network for machining tool wear prediction publication-title: J. Manuf. Syst. – volume: 9 start-page: 3473 year: 2019 ident: bib0045 article-title: Information fusion for multi-source material data: progress and challenges publication-title: Appl. Sci. – start-page: 1 year: 2021 end-page: 6 ident: bib0140 article-title: Physics-informed neural networks for non-linear system identification for power system dynamics publication-title: 2021 IEEE Madrid PowerTech – volume: 54 start-page: 62 year: 2020 end-page: 73 ident: bib0057 article-title: A nmf-based extraction of physically meaningful components from sensory data of metal casting processes publication-title: J. Manuf. Syst. – year: 2021 ident: bib0133 article-title: Parameter identification for a damage model using a physics informed neural network publication-title: arXiv preprint arXiv:2107.08781 – year: 2020 ident: bib0079 article-title: Cyber-attack detection for electric vehicles using physics-guided machine learning publication-title: IEEE Trans. Transp. Electrif. – volume: 10 year: 2019 ident: bib0182 article-title: A new hybrid prognostic methodology publication-title: Int. J. Prognost. Health Manag. – volume: 62 start-page: 17 year: 2022 end-page: 27 ident: bib0034 article-title: Physics-informed meta learning for machining tool wear prediction publication-title: J. Manuf. Syst. – year: 2020 ident: bib0128 article-title: Physics-informed machine learning for sensor fault detection with flight test data publication-title: arXiv preprint arXiv:2006.13380 – volume: 96 start-page: 103996 year: 2020 ident: bib0120 article-title: A tutorial on solving ordinary differential equations using python and hybrid physics-informed neural network publication-title: Eng. Appl. Artif. Intell. – reference: R. Giorgiani do Nascimento, Hybrid physics-informed neural networks for dynamical systems (2020). – volume: 167 start-page: 108575 year: 2022 ident: bib0167 article-title: A health-adaptive time-scale representation (htsr) embedded convolutional neural network for gearbox fault diagnostics publication-title: Mech. Syst. Signal. Process. – year: 2019 ident: bib0011 article-title: Digital twin: values, challenges and enablers publication-title: arXiv preprint arXiv:1910.01719 – reference: A.I. Ozdagli, X. Koutsoukos, Model-based damage detection through physics-guided learning for dynamic systems. – volume: 101 start-page: 1 year: 2021 end-page: 21 ident: bib0102 article-title: Controlling draft interactions between quadcopter unmanned aerial vehicles with physics-aware modeling publication-title: J. Intell. Robot. Syst. – year: 2020 ident: bib0217 article-title: Universal battery performance and degradation model for electric aircraft publication-title: arXiv preprint arXiv:2008.01527 – year: 2020 ident: bib0071 article-title: The application of data-driven methods and physics-based learning for improving battery safety publication-title: Joule – volume: 112 start-page: 108705 year: 2020 ident: bib0105 article-title: Physics informed topology learning in networks of linear dynamical systems publication-title: Automatica – reference: P. P.Bonissone, Prognostics & health management at ge, ( – volume: 59 start-page: 522 year: 2021 end-page: 534 ident: bib0076 article-title: Physics-guided logistic classification for tool life modeling and process parameter optimization in machining publication-title: J. Manuf. Syst. – volume: 445 start-page: 110624 year: 2021 ident: bib0090 article-title: Theory-guided hard constraint projection (hcp): aknowledge-based data-driven scientific machine learning method publication-title: J. Comput. Phys. – volume: 166 start-page: A3189 year: 2019 ident: bib0017 article-title: Review and performance comparison of mechanical-chemical degradation models for lithium-ion batteries publication-title: J. Electrochem. Soc. – year: 2020 ident: bib0069 article-title: Learning mesh-based simulation with graph networks publication-title: arXiv preprint arXiv:2010.03409 – volume: 404 start-page: 109136 year: 2020 ident: bib0108 article-title: Adaptive activation functions accelerate convergence in deep and physics-informed neural networks publication-title: J. Comput. Phys. – year: 2021 ident: bib0082 article-title: Physics-guided deep learning for dynamical systems: asurvey publication-title: arXiv preprint arXiv:2107.01272 – volume: 185 start-page: 109772 year: 2023 ident: bib0171 article-title: Deep convolutional generative adversarial network with semi-supervised learning enabled physics elucidation for extended gear fault diagnosis under data limitations publication-title: Mech. Syst. Signal. Process. – volume: 217 start-page: 108114 year: 2022 ident: bib0062 article-title: Knowledge-based data augmentation of small samples for oil condition prediction publication-title: Reliab. Eng. Syst. Saf. – volume: 229 start-page: 108898 year: 2023 ident: bib0188 article-title: Physics-informed machine learning for degradation modeling of an electro-hydrostatic actuator system publication-title: Reliab. Eng. Syst. Saf. – volume: 137 start-page: 104568 year: 2021 ident: bib0142 article-title: Visualization of hydraulic fracture using physics-informed clustering to process ultrasonic shear waves publication-title: Int. J. Rock Mech. Min. Sci. – year: 2019 ident: bib0152 article-title: Physics-informed machine learning method for forecasting and uncertainty quantification of partially observed and unobserved states in power grids publication-title: Proceedings of the 52nd Hawaii International Conference on System Sciences – volume: 232 start-page: 111882 year: 2021 ident: bib0005 article-title: An improved time-varying empirical mode decomposition for structural condition assessment using limited sensors publication-title: Eng. Struct. – year: 2018 ident: bib0111 article-title: Physics-informed kriging: a physics-informed gaussian process regression method for data-model convergence publication-title: arXiv preprint arXiv:1809.03461 – year: 2017 ident: bib0094 article-title: Physics-guided neural networks (pgnn): an application in lake temperature modeling publication-title: arXiv preprint arXiv:1710.11431 – start-page: arXiv year: 2020 end-page: 2004 ident: bib0103 article-title: On the convergence and generalization of physics informed neural networks publication-title: arXiv e-prints – volume: 34 start-page: 1 year: 2021 end-page: 21 ident: bib0191 article-title: Deep learning-driven data curation and model interpretation for smart manufacturing publication-title: Chinese J. Mech. Eng. – volume: 65 start-page: 354 year: 2022 ident: bib0192 article-title: A comprehensive review of digital twin–part 1: modeling and twinning enabling technologies publication-title: Struct. Multidiscip. Optim. – volume: 204 start-page: 107194 year: 2020 ident: bib0205 article-title: Modernizing risk assessment: a systematic integration of pra and phm techniques publication-title: Reliab. Eng. Syst. Saf. – volume: 258 start-page: 106678 year: 2022 ident: bib0109 article-title: Recurrent neural network-based multiaxial plasticity model with regularization for physics-informed constraints publication-title: Comput. Struct. – volume: 253 start-page: 113525 year: 2019 ident: bib0012 article-title: Modeling the performance of a sorption thermal energy storage reactor using artificial neural networks publication-title: Appl. Energy – start-page: 34 year: 2019 end-page: 41 ident: bib0038 article-title: Pi-lstm: Physics-infused long short-term memory network publication-title: 2019 18th IEEE International Conference on Machine Learning and Applications (ICMLA) – volume: 6 start-page: 04020013 year: 2020 ident: bib0118 article-title: A data-driven physics-informed method for prognosis of infrastructure systems: theory and application to crack prediction publication-title: ASCE-ASME J. Risk Uncertain. Eng. Syst., Part A: Civil Eng. – volume: 254 start-page: 111299 year: 2022 ident: bib0146 article-title: Physics-informed turbulence intensity infusion: a new hybrid approach for marine current turbine rotor blade fault detection publication-title: Ocean Eng. – volume: 18 start-page: 1629 year: 2021 end-page: 1640 ident: bib0130 article-title: Development of a physics-informed doubly fed cross-residual deep neural network for high-precision magnetic flux leakage defect size estimation publication-title: IEEE Trans. Ind. Inf. – volume: 428 start-page: 116 year: 2021 end-page: 129 ident: bib0047 article-title: Midphynet: memorized infusion of decomposed physics in neural networks to model dynamic systems publication-title: Neurocomputing – volume: 12 year: 2021 ident: bib0178 article-title: Remaining useful life estimation using neural ordinary differential equations publication-title: Int. J. Prognost. Health Manag. – start-page: 1444 year: 2019 end-page: 1449 ident: bib0194 article-title: Data preparation and preprocessing for broadcast systems monitoring in phm framework publication-title: 2019 6th International Conference on Control, Decision and Information Technologies (CoDIT) – volume: 378 start-page: 686 year: 2019 end-page: 707 ident: bib0021 article-title: Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations publication-title: J. Comput. Phys. – volume: 52 start-page: 2302 year: 2021 end-page: 2312 ident: bib0172 article-title: Waveletkernelnet: an interpretable deep neural network for industrial intelligent diagnosis publication-title: IEEE Trans. Syst. Man Cybern.: Syst. – volume: 169 start-page: 108779 year: 2022 ident: bib0168 article-title: Interpretable online updated weights: optimized square envelope spectrum for machine condition monitoring and fault diagnosis publication-title: Mech. Syst. Signal Process. – volume: 28 start-page: 3801 year: 2021 end-page: 3830 ident: bib0125 article-title: A survey of bayesian calibration and physics-informed neural networks in scientific modeling publication-title: Arch. Comput. Methods Eng. – volume: 508 start-page: 116196 year: 2021 ident: bib0156 article-title: Structural identification with physics-informed neural ordinary differential equations publication-title: J. Sound Vib. – volume: 57 start-page: 395 year: 2015 end-page: 400 ident: bib0198 article-title: Synthetic data generation in hybrid modelling of rolling element bearings publication-title: Insight-Non-Destruct. Test. Condit. Monitor. – start-page: 1 year: 2021 end-page: 16 ident: bib0049 article-title: Prognostics-based adaptive control strategy for lifetime control of wind turbines publication-title: Wind Energy Sci. Discuss. – volume: 8 year: 2016 ident: bib0056 article-title: A review of physics-based models in prognostics: application to gears and bearings of rotating machinery publication-title: Adv. Mech. Eng. – year: 2018 ident: bib0068 article-title: Physics-based learning for aircraft dynamics simulation publication-title: 10th Annual Conference of the Prognostics and Health Management Society, PHM 2018 – volume: 65 start-page: 4290 year: 2017 end-page: 4300 ident: bib0213 article-title: Deep residual networks with dynamically weighted wavelet coefficients for fault diagnosis of planetary gearboxes publication-title: IEEE Trans. Ind. Electron. – volume: 31 start-page: 275 year: 2020 end-page: 298 ident: bib0202 article-title: Managing computational complexity using surrogate models: a critical review publication-title: Res. Eng. Des. – start-page: 1 year: 2021 end-page: 12 ident: bib0189 article-title: Knowledge integration into deep learning in dynamical systems: an overview and taxonomy publication-title: J. Mech. Sci. Technol. – year: 2021 ident: bib0039 article-title: Multi-fidelity information fusion with concatenated neural networks publication-title: arXiv preprint arXiv:2110.04170 – year: 2020 ident: bib0058 article-title: Increasing facility uptime using machine learning and physics-based hybrid analytics in a dynamic digital twin publication-title: Offshore Technology Conference – volume: 513 start-page: 230526 year: 2021 ident: bib0113 article-title: Hybrid physics-informed neural networks for lithium-ion battery modeling and prognosis publication-title: J. Power Sources – volume: 26 start-page: e2358 year: 2019 ident: bib0088 article-title: Estimation of full-field, full-order experimental modal model of cable vibration from digital video measurements with physics-guided unsupervised machine learning and computer vision publication-title: Struct. Control Health Monitor. – start-page: 1149 year: 2020 ident: bib0117 article-title: Physics-informed neural networks for bias compensation in corrosion-fatigue publication-title: AIAA Scitech 2020 Forum – volume: 185 start-page: 109760 year: 2023 ident: bib0200 article-title: Fleet-based early fault detection of wind turbine gearboxes using physics-informed deep learning based on cyclic spectral coherence publication-title: Mech. Syst. Signal Process. – volume: 107 start-page: 241 year: 2018 end-page: 265 ident: bib0007 article-title: A review on the application of deep learning in system health management publication-title: Mech. Syst. Signal Process. – volume: 168 start-page: 114316 year: 2021 ident: bib0092 article-title: Probabilistic physics-guided machine learning for fatigue data analysis publication-title: Expert Syst. Appl. – volume: volume 11 year: 2019 ident: bib0121 article-title: Physics-informed neural networks for corrosion-fatigue prognosis publication-title: Proceedings of the Annual Conference of the PHM Society – start-page: 0418 year: 2020 ident: bib0195 article-title: Toward predictive digital twins via component-based reduced-order models and interpretable machine learning publication-title: AIAA Scitech 2020 Forum – volume: 215 start-page: 107938 year: 2021 ident: bib0170 article-title: Intelligent fault diagnosis of machinery using digital twin-assisted deep transfer learning publication-title: Reliab. Eng. Syst. Saf. – volume: 146 start-page: 268 year: 2019 end-page: 278 ident: bib0208 article-title: A deep convolutional neural network based fusion method of two-direction vibration signal data for health state identification of planetary gearboxes publication-title: Measurement – volume: 383 start-page: 113885 year: 2021 ident: bib0070 article-title: A phase field and deep-learning based approach for accurate prediction of structural residual useful life publication-title: Comput. Methods Appl. Mech. Eng. – volume: 181 start-page: 109631 year: 2021 ident: bib0173 article-title: Gas path fault diagnosis for gas turbine group based on deep transfer learning publication-title: Measurement – year: 2019 ident: bib0013 article-title: Hybrid deep fault detection and isolation: combining deep neural networks and system performance models publication-title: arXiv preprint arXiv:1908.01529 – volume: 44 start-page: 191 year: 2016 end-page: 199 ident: bib0051 article-title: A hybrid framework combining data-driven and model-based methods for system remaining useful life prediction publication-title: Appl. Soft. Comput. – volume: volume 11423 start-page: 114230K year: 2020 ident: bib0101 article-title: Data fusion methods for materials awareness publication-title: Signal Processing, Sensor/Information Fusion, and Target Recognition XXIX – volume: 1338 start-page: 28 year: 2021 ident: bib0144 article-title: Quantifying uncertainty in physics-informed variational autoencoders for anomaly detection publication-title: Impact and Opportunities of Artificial Intelligence Techniques in the Steel Industry: Ongoing Applications, Perspectives and Future Trends – volume: 11 year: 2020 ident: bib0116 article-title: A physics-informed neural network for wind turbine main bearing fatigue publication-title: Int. J. Prognostic. Health Manag. – reference: With physics-informed ai, machine operators can trust and verify, ( – start-page: 3046 year: 2021 ident: bib0179 article-title: Usage-based lifing of lithium-ion battery with hybrid physics-informed neural networks publication-title: AIAA AVIATION 2021 FORUM – volume: 112 start-page: 102872 year: 2021 ident: bib0077 article-title: A physics-guided machine learning model for two-dimensional structures based on ordinary state-based peridynamics publication-title: Theor. Appl. Fract. Mech. – year: 2020 ident: bib0209 article-title: A physics model embedded hybrid deep neural network for drillstring washout detection publication-title: IADC/SPE International Drilling Conference and Exhibition – volume: 66 start-page: 1 year: 2023 ident: bib0193 article-title: A comprehensive review of digital twin–part 2: roles of uncertainty quantification and optimization, a battery digital twin, and perspectives publication-title: Struct. Multidiscip. Optim. – year: 2021 ident: bib0157 article-title: Characterizing possible failure modes in physics-informed neural networks publication-title: arXiv preprint arXiv:2109.01050 – volume: 363 start-page: 112892 year: 2020 ident: bib0091 article-title: Fea-net: a physics-guided data-driven model for efficient mechanical response prediction publication-title: Comput. Methods Appl. Mech. Eng. – year: 2019 ident: bib0099 article-title: Physics-aware difference graph networks for sparsely-observed dynamics publication-title: International Conference on Learning Representations – reference: W.Z. Levente Klein, Peeking into ai’s ‘black box’ brain – with physics, ( – start-page: 28 year: 2020 end-page: 38 ident: bib0145 article-title: Quantifying uncertainty in physics-informed variational autoencoders for anomaly detection publication-title: Cybersecurity workshop by European Steel Technology Platform – year: 2021 ident: bib0075 article-title: A physics-guided deep learning predictive model for robust production forecasting and diagnostics in unconventional wells publication-title: SPE/AAPG/SEG Unconventional Resources Technology Conference – year: 2023 ident: bib0197 article-title: Machine fault classification using hamiltonian neural networks publication-title: arXiv preprint arXiv:2301.02243 – volume: 24 start-page: 100797 year: 2020 ident: bib0046 article-title: Coupling in situ experiments and modeling–opportunities for data fusion, machine learning, and discovery of emergent behavior publication-title: Curr. Opin. Solid State Mater. Sci. – volume: 229 start-page: 111582 year: 2021 ident: bib0050 article-title: Dynnet: physics-based neural architecture design for nonlinear structural response modeling and prediction publication-title: Eng. Struct. – volume: 151 start-page: 107374 year: 2021 ident: bib0112 article-title: Phymdan: physics-informed knowledge transfer between buildings for seismic damage diagnosis through adversarial learning publication-title: Mech. Syst. Signal Process. – start-page: 1 year: 2021 end-page: 5 ident: bib0033 article-title: A physics-informed transfer learning approach for anomaly detection of aerospace cmg with limited telemetry data publication-title: 2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing) – reference: H.E. Sánchez Sardi, Prognostics and health aware model predictive control of wind turbines (2017). – volume: 8 year: 2017 ident: bib0052 article-title: Multi-physics based simulations of an oleo-pneumatic shock absorber system for phm publication-title: Int. J. Prognost. Health Manag. – volume: 70 start-page: 1 year: 2021 end-page: 28 ident: bib0030 article-title: Applications of unsupervised deep transfer learning to intelligent fault diagnosis: a survey and comparative study publication-title: IEEE Trans. Instrum. Meas. – volume: 417 start-page: 36 year: 2020 end-page: 63 ident: bib0207 article-title: A comprehensive review on convolutional neural network in machine fault diagnosis publication-title: Neurocomputing – volume: 38 start-page: 119 year: 2019 end-page: 131 ident: bib0211 article-title: Fault location in power distribution systems via deep graph convolutional networks publication-title: IEEE J. Sel. Areas Commun. – year: 2022 ident: bib0123 article-title: Knowledge informed machine learning using a weibull-based loss function publication-title: arXiv preprint arXiv:2201.01769 – volume: 1 start-page: 1 year: 2020 end-page: 34 ident: bib0032 article-title: Integrating physics-based modeling with machine learning: a survey publication-title: arXiv preprint arXiv:2003.04919 – year: 2022 ident: bib0176 article-title: Physics informed neural network for dynamic stress prediction publication-title: arXiv preprint arXiv:2211.16190 – volume: 139 year: 2017 ident: bib0206 article-title: Orthogonal analysis of multisensor data fusion for improved quality control publication-title: J. Manuf. Sci. Eng. – year: 2021 ident: bib0009 article-title: Explainable ai (xai) for phm of industrial asset: astate-of-the-art, prisma-compliant systematic review publication-title: arXiv preprint arXiv:2107.03869 – year: 2019 ident: bib0104 article-title: Variational physics-informed neural networks for solving partial differential equations publication-title: arXiv preprint arXiv:1912.00873 – volume: 8 start-page: 431 year: 2020 end-page: 449 ident: bib0073 article-title: Physics-guided deep learning for drag force prediction in dense fluid-particulate systems publication-title: Big Data – start-page: 108900 year: 2022 ident: bib0029 article-title: Physics-informed machine learning for reliability and systems safety applications: state of the art and challenges publication-title: Reliab. Eng. Syst. Saf. – start-page: 1860 year: 2020 ident: bib0053 article-title: Physics-based deep learning for probabilistic fracture analysis of composite materials publication-title: AIAA Scitech 2020 Forum – year: 2020 ident: bib0199 article-title: Knowledge transfer between buildings for seismic damage diagnosis through adversarial learning publication-title: arXiv preprint arXiv:2002.09513 – volume: 58 start-page: 5459 year: 2020 end-page: 5471 ident: bib0186 article-title: Cumulative damage modeling with recurrent neural networks publication-title: AIAA J. – year: 2021 ident: bib0020 article-title: Physics-informed graphical neural network for parameter & state estimations in power systems publication-title: arXiv preprint arXiv:2102.06349 – year: 2021 ident: bib0114 article-title: A physics-informed variational deeponet for predicting the crack path in brittle materials publication-title: arXiv preprint arXiv:2108.06905 – start-page: 1 year: 2019 end-page: 6 ident: bib0150 article-title: Wave physics informed dictionary learning in one dimension publication-title: 2019 IEEE 29th International Workshop on Machine Learning for Signal Processing (MLSP) – reference: A.D.P. Dourado, F. Viana, Ensemble of hybrid neural networks to compensate for epistemic uncertainties: A case study in system prognosis (2021). – volume: 15 start-page: 1 year: 2022 end-page: 16 ident: bib0060 article-title: Shrinkage porosity prediction empowered by physics-based and data-driven hybrid models publication-title: Int. J. Mater. Form. – volume: 440 start-page: 110414 year: 2021 ident: bib0098 article-title: Solving inverse-pde problems with physics-aware neural networks publication-title: J. Comput. Phys. – volume: 167 start-page: 108575 year: 2022 ident: 10.1016/j.apm.2023.07.011_bib0167 article-title: A health-adaptive time-scale representation (htsr) embedded convolutional neural network for gearbox fault diagnostics publication-title: Mech. Syst. Signal. Process. doi: 10.1016/j.ymssp.2021.108575 – volume: 168 start-page: 114316 year: 2021 ident: 10.1016/j.apm.2023.07.011_bib0092 article-title: Probabilistic physics-guided machine learning for fatigue data analysis publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2020.114316 – volume: 108 start-page: 33 year: 2018 ident: 10.1016/j.apm.2023.07.011_bib0204 article-title: Artificial intelligence for fault diagnosis of rotating machinery: a review publication-title: Mech. Syst. Signal Process. doi: 10.1016/j.ymssp.2018.02.016 – volume: 215 start-page: 107938 year: 2021 ident: 10.1016/j.apm.2023.07.011_bib0170 article-title: Intelligent fault diagnosis of machinery using digital twin-assisted deep transfer learning publication-title: Reliab. Eng. Syst. Saf. doi: 10.1016/j.ress.2021.107938 – start-page: 5919 year: 2018 ident: 10.1016/j.apm.2023.07.011_bib0064 article-title: A physics-based deep learning approach for fault diagnosis of rotating machinery – start-page: arXiv year: 2019 ident: 10.1016/j.apm.2023.07.011_bib0158 article-title: Fleet prognosis with physics-informed recurrent neural networks publication-title: arXiv e-prints – year: 2020 ident: 10.1016/j.apm.2023.07.011_bib0079 article-title: Cyber-attack detection for electric vehicles using physics-guided machine learning publication-title: IEEE Trans. Transp. Electrif. – volume: 101 start-page: 2861 issue: 9 year: 2019 ident: 10.1016/j.apm.2023.07.011_bib0043 article-title: Hybrid data-driven physics-based model fusion framework for tool wear prediction publication-title: Int. J. Adv. Manuf. Technol. doi: 10.1007/s00170-018-3157-5 – volume: 31 start-page: 275 issue: 3 year: 2020 ident: 10.1016/j.apm.2023.07.011_bib0202 article-title: Managing computational complexity using surrogate models: a critical review publication-title: Res. Eng. Des. doi: 10.1007/s00163-020-00336-7 – volume: 38 start-page: 119 issue: 1 year: 2019 ident: 10.1016/j.apm.2023.07.011_bib0211 article-title: Fault location in power distribution systems via deep graph convolutional networks publication-title: IEEE J. Sel. Areas Commun. doi: 10.1109/JSAC.2019.2951964 – volume: 72 start-page: 624 year: 2018 ident: 10.1016/j.apm.2023.07.011_bib0065 article-title: Physics of failure-based reliability prediction of turbine blades using multi-source information fusion publication-title: Appl. Soft. Comput. doi: 10.1016/j.asoc.2018.05.015 – year: 2021 ident: 10.1016/j.apm.2023.07.011_bib0067 article-title: Cylindrical battery fault detection under extreme fast charging: a physics-based learning approach publication-title: arXiv preprint arXiv:2105.02169 – year: 2019 ident: 10.1016/j.apm.2023.07.011_bib0151 article-title: Subsurface monitoring via physics-informed deep neural network analysis of das – volume: 513 start-page: 230526 year: 2021 ident: 10.1016/j.apm.2023.07.011_bib0113 article-title: Hybrid physics-informed neural networks for lithium-ion battery modeling and prognosis publication-title: J. Power Sources doi: 10.1016/j.jpowsour.2021.230526 – volume: 11 issue: 1 year: 2020 ident: 10.1016/j.apm.2023.07.011_bib0116 article-title: A physics-informed neural network for wind turbine main bearing fatigue publication-title: Int. J. Prognostic. Health Manag. doi: 10.36001/ijphm.2020.v11i1.2594 – volume: 57 start-page: 115 year: 2020 ident: 10.1016/j.apm.2023.07.011_bib0042 article-title: A survey on machine learning for data fusion publication-title: Inf. Fusion doi: 10.1016/j.inffus.2019.12.001 – ident: 10.1016/j.apm.2023.07.011_bib0024 – year: 2020 ident: 10.1016/j.apm.2023.07.011_bib0218 article-title: Dynet: dynamic convolution for accelerating convolutional neural networks publication-title: arXiv preprint arXiv:2004.10694 – volume: 132 start-page: 103523 year: 2021 ident: 10.1016/j.apm.2023.07.011_bib0016 article-title: Computational framework for real-time diagnostics and prognostics of aircraft actuation systems publication-title: Comput. Ind. doi: 10.1016/j.compind.2021.103523 – volume: 65 start-page: 4290 issue: 5 year: 2017 ident: 10.1016/j.apm.2023.07.011_bib0213 article-title: Deep residual networks with dynamically weighted wavelet coefficients for fault diagnosis of planetary gearboxes publication-title: IEEE Trans. Ind. Electron. doi: 10.1109/TIE.2017.2762639 – volume: 302 start-page: 117472 year: 2022 ident: 10.1016/j.apm.2023.07.011_bib0059 article-title: Towards a generic physics-based machine learning model for geometry invariant thermal history prediction in additive manufacturing publication-title: J. Mater. Process. Technol. doi: 10.1016/j.jmatprotec.2021.117472 – start-page: 3046 year: 2021 ident: 10.1016/j.apm.2023.07.011_bib0179 article-title: Usage-based lifing of lithium-ion battery with hybrid physics-informed neural networks – volume: 254 start-page: 111299 year: 2022 ident: 10.1016/j.apm.2023.07.011_bib0146 article-title: Physics-informed turbulence intensity infusion: a new hybrid approach for marine current turbine rotor blade fault detection publication-title: Ocean Eng. doi: 10.1016/j.oceaneng.2022.111299 – volume: 18 start-page: 1629 issue: 3 year: 2021 ident: 10.1016/j.apm.2023.07.011_bib0130 article-title: Development of a physics-informed doubly fed cross-residual deep neural network for high-precision magnetic flux leakage defect size estimation publication-title: IEEE Trans. Ind. Inf. doi: 10.1109/TII.2021.3089333 – volume: 145 start-page: 642 year: 2020 ident: 10.1016/j.apm.2023.07.011_bib0036 article-title: An integrated fault diagnosis and prognosis approach for predictive maintenance of wind turbine bearing with limited samples publication-title: Renew. Energy doi: 10.1016/j.renene.2019.06.103 – year: 2019 ident: 10.1016/j.apm.2023.07.011_bib0104 article-title: Variational physics-informed neural networks for solving partial differential equations publication-title: arXiv preprint arXiv:1912.00873 – volume: 426 start-page: 109942 year: 2021 ident: 10.1016/j.apm.2023.07.011_bib0159 article-title: Transfer learning based multi-fidelity physics informed deep neural network publication-title: J. Comput. Phys. doi: 10.1016/j.jcp.2020.109942 – year: 2019 ident: 10.1016/j.apm.2023.07.011_bib0011 article-title: Digital twin: values, challenges and enablers publication-title: arXiv preprint arXiv:1910.01719 – volume: 59 start-page: 522 year: 2021 ident: 10.1016/j.apm.2023.07.011_bib0076 article-title: Physics-guided logistic classification for tool life modeling and process parameter optimization in machining publication-title: J. Manuf. Syst. doi: 10.1016/j.jmsy.2021.03.025 – year: 2019 ident: 10.1016/j.apm.2023.07.011_bib0013 article-title: Hybrid deep fault detection and isolation: combining deep neural networks and system performance models publication-title: arXiv preprint arXiv:1908.01529 – volume: 96 start-page: 103996 year: 2020 ident: 10.1016/j.apm.2023.07.011_bib0120 article-title: A tutorial on solving ordinary differential equations using python and hybrid physics-informed neural network publication-title: Eng. Appl. Artif. Intell. doi: 10.1016/j.engappai.2020.103996 – volume: 204 start-page: 107194 year: 2020 ident: 10.1016/j.apm.2023.07.011_bib0205 article-title: Modernizing risk assessment: a systematic integration of pra and phm techniques publication-title: Reliab. Eng. Syst. Saf. doi: 10.1016/j.ress.2020.107194 – volume: 185 start-page: 109760 year: 2023 ident: 10.1016/j.apm.2023.07.011_bib0200 article-title: Fleet-based early fault detection of wind turbine gearboxes using physics-informed deep learning based on cyclic spectral coherence publication-title: Mech. Syst. Signal Process. doi: 10.1016/j.ymssp.2022.109760 – volume: volume 1949 start-page: 020023 year: 2018 ident: 10.1016/j.apm.2023.07.011_bib0048 article-title: The role of data fusion in predictive maintenance using digital twin doi: 10.1063/1.5031520 – year: 2021 ident: 10.1016/j.apm.2023.07.011_bib0163 article-title: Physics-guided machine learning approach to characterizing small-scale fractures in geothermal fields – volume: 508 start-page: 116196 year: 2021 ident: 10.1016/j.apm.2023.07.011_bib0156 article-title: Structural identification with physics-informed neural ordinary differential equations publication-title: J. Sound Vib. doi: 10.1016/j.jsv.2021.116196 – year: 2020 ident: 10.1016/j.apm.2023.07.011_bib0128 article-title: Physics-informed machine learning for sensor fault detection with flight test data publication-title: arXiv preprint arXiv:2006.13380 – ident: 10.1016/j.apm.2023.07.011_bib0023 – volume: 106 start-page: 102447 year: 2020 ident: 10.1016/j.apm.2023.07.011_bib0147 article-title: Transfer learning enhanced physics informed neural network for phase-field modeling of fracture publication-title: Theor. Appl. Fract. Mech. doi: 10.1016/j.tafmec.2019.102447 – volume: 232 start-page: 111882 year: 2021 ident: 10.1016/j.apm.2023.07.011_bib0005 article-title: An improved time-varying empirical mode decomposition for structural condition assessment using limited sensors publication-title: Eng. Struct. doi: 10.1016/j.engstruct.2021.111882 – start-page: 347 year: 2022 ident: 10.1016/j.apm.2023.07.011_bib0135 article-title: Physics-informed Machine Learning for Structural Health Monitoring – volume: 112 start-page: 102872 year: 2021 ident: 10.1016/j.apm.2023.07.011_bib0077 article-title: A physics-guided machine learning model for two-dimensional structures based on ordinary state-based peridynamics publication-title: Theor. Appl. Fract. Mech. doi: 10.1016/j.tafmec.2020.102872 – volume: volume 11 year: 2019 ident: 10.1016/j.apm.2023.07.011_bib0148 article-title: Wind turbine main bearing fatigue life estimation with physicsinformed neural networks – volume: 52 start-page: 2302 issue: 4 year: 2021 ident: 10.1016/j.apm.2023.07.011_bib0172 article-title: Waveletkernelnet: an interpretable deep neural network for industrial intelligent diagnosis publication-title: IEEE Trans. Syst. Man Cybern.: Syst. doi: 10.1109/TSMC.2020.3048950 – volume: 16 start-page: 065003 issue: 6 year: 2021 ident: 10.1016/j.apm.2023.07.011_bib0137 article-title: Physics-informed generative neural network: an application to troposphere temperature prediction publication-title: Environ. Res. Lett. doi: 10.1088/1748-9326/abfde9 – volume: 185 start-page: 109772 year: 2023 ident: 10.1016/j.apm.2023.07.011_bib0171 article-title: Deep convolutional generative adversarial network with semi-supervised learning enabled physics elucidation for extended gear fault diagnosis under data limitations publication-title: Mech. Syst. Signal. Process. doi: 10.1016/j.ymssp.2022.109772 – year: 2017 ident: 10.1016/j.apm.2023.07.011_bib0001 – volume: 15 start-page: 1 issue: 3 year: 2022 ident: 10.1016/j.apm.2023.07.011_bib0060 article-title: Shrinkage porosity prediction empowered by physics-based and data-driven hybrid models publication-title: Int. J. Mater. Form. doi: 10.1007/s12289-022-01677-5 – start-page: 106352 year: 2021 ident: 10.1016/j.apm.2023.07.011_bib0055 article-title: A probabilistic fatigue life prediction for adhesively bonded joints via anns-based hybrid model publication-title: Int. J. Fatigue doi: 10.1016/j.ijfatigue.2021.106352 – year: 2021 ident: 10.1016/j.apm.2023.07.011_bib0114 article-title: A physics-informed variational deeponet for predicting the crack path in brittle materials publication-title: arXiv preprint arXiv:2108.06905 – year: 2023 ident: 10.1016/j.apm.2023.07.011_bib0197 article-title: Machine fault classification using hamiltonian neural networks publication-title: arXiv preprint arXiv:2301.02243 – year: 2021 ident: 10.1016/j.apm.2023.07.011_bib0010 – volume: 166 start-page: A3189 issue: 14 year: 2019 ident: 10.1016/j.apm.2023.07.011_bib0017 article-title: Review and performance comparison of mechanical-chemical degradation models for lithium-ion batteries publication-title: J. Electrochem. Soc. doi: 10.1149/2.0281914jes – volume: 112 start-page: 103114 year: 2019 ident: 10.1016/j.apm.2023.07.011_bib0014 article-title: Dynamic condition monitoring method based on dimensionality reduction techniques for data-limited industrial environments publication-title: Comput. Ind. doi: 10.1016/j.compind.2019.07.004 – volume: 57 start-page: 298 year: 2020 ident: 10.1016/j.apm.2023.07.011_bib0078 article-title: Physics guided neural network for machining tool wear prediction publication-title: J. Manuf. Syst. doi: 10.1016/j.jmsy.2020.09.005 – volume: 39 start-page: 1 issue: 3 year: 2020 ident: 10.1016/j.apm.2023.07.011_bib0037 article-title: Physics-informed neural network for ultrasound nondestructive quantification of surface breaking cracks publication-title: J. Nondestr. Eval. doi: 10.1007/s10921-020-00705-1 – year: 2021 ident: 10.1016/j.apm.2023.07.011_bib0075 article-title: A physics-guided deep learning predictive model for robust production forecasting and diagnostics in unconventional wells – volume: 253 start-page: 113525 year: 2019 ident: 10.1016/j.apm.2023.07.011_bib0012 article-title: Modeling the performance of a sorption thermal energy storage reactor using artificial neural networks publication-title: Appl. Energy doi: 10.1016/j.apenergy.2019.113525 – start-page: 1 year: 2021 ident: 10.1016/j.apm.2023.07.011_bib0049 article-title: Prognostics-based adaptive control strategy for lifetime control of wind turbines publication-title: Wind Energy Sci. Discuss. – year: 2021 ident: 10.1016/j.apm.2023.07.011_bib0096 article-title: Deepphysics: a physics aware deep learning framework for real-time simulation publication-title: arXiv preprint arXiv:2109.09491 – start-page: 8157 year: 2019 ident: 10.1016/j.apm.2023.07.011_bib0212 article-title: A recurrent graph neural network for multi-relational data – year: 2020 ident: 10.1016/j.apm.2023.07.011_bib0071 article-title: The application of data-driven methods and physics-based learning for improving battery safety publication-title: Joule – ident: 10.1016/j.apm.2023.07.011_bib0155 doi: 10.33737/jgpps/134845 – volume: 101 start-page: 1 issue: 1 year: 2021 ident: 10.1016/j.apm.2023.07.011_bib0102 article-title: Controlling draft interactions between quadcopter unmanned aerial vehicles with physics-aware modeling publication-title: J. Intell. Robot. Syst. doi: 10.1007/s10846-020-01295-w – year: 2021 ident: 10.1016/j.apm.2023.07.011_bib0082 article-title: Physics-guided deep learning for dynamical systems: asurvey publication-title: arXiv preprint arXiv:2107.01272 – ident: 10.1016/j.apm.2023.07.011_bib0097 doi: 10.1002/acs.2784 – volume: 179 start-page: 704 year: 2019 ident: 10.1016/j.apm.2023.07.011_bib0066 article-title: Projection-based model reduction: formulations for physics-based machine learning publication-title: Comput. Fluids doi: 10.1016/j.compfluid.2018.07.021 – volume: 50 start-page: 101404 year: 2021 ident: 10.1016/j.apm.2023.07.011_bib0018 article-title: A survey of modeling for prognosis and health management of industrial equipment publication-title: Adv. Eng. Inf. doi: 10.1016/j.aei.2021.101404 – volume: 8 start-page: 29857 year: 2020 ident: 10.1016/j.apm.2023.07.011_bib0004 article-title: Deep learning algorithms for bearing fault diagnostics–a comprehensive review publication-title: IEEE Access doi: 10.1109/ACCESS.2020.2972859 – ident: 10.1016/j.apm.2023.07.011_bib0028 – start-page: 109067 year: 2023 ident: 10.1016/j.apm.2023.07.011_bib0214 article-title: Sensor fault detection of vehicle suspension systems based on transmissibility operators and neyman–pearson test publication-title: Reliab. Eng. Syst. Saf. doi: 10.1016/j.ress.2022.109067 – start-page: arXiv year: 2020 ident: 10.1016/j.apm.2023.07.011_bib0103 article-title: On the convergence and generalization of physics informed neural networks publication-title: arXiv e-prints – volume: 168 start-page: 108709 year: 2022 ident: 10.1016/j.apm.2023.07.011_bib0131 article-title: Physics-informed deep learning for signal compression and reconstruction of big data in industrial condition monitoring publication-title: Mech. Syst. Signal Process. doi: 10.1016/j.ymssp.2021.108709 – volume: 142 issue: 6 year: 2020 ident: 10.1016/j.apm.2023.07.011_bib0175 article-title: Damage localization in pressure vessel by guided waves based on convolution neural network approach publication-title: J. Press. Vessel Technol. doi: 10.1115/1.4047213 – start-page: 1 year: 2021 ident: 10.1016/j.apm.2023.07.011_bib0033 article-title: A physics-informed transfer learning approach for anomaly detection of aerospace cmg with limited telemetry data – volume: 20 start-page: 1494 issue: 4 year: 2021 ident: 10.1016/j.apm.2023.07.011_bib0215 article-title: Attribute-based structural damage identification by few-shot meta learning with inter-class knowledge transfer publication-title: Struct. Health Monitor. doi: 10.1177/1475921720921135 – year: 2021 ident: 10.1016/j.apm.2023.07.011_bib0133 article-title: Parameter identification for a damage model using a physics informed neural network publication-title: arXiv preprint arXiv:2107.08781 – year: 2019 ident: 10.1016/j.apm.2023.07.011_bib0141 article-title: Informed machine learning–a taxonomy and survey of integrating knowledge into learning systems publication-title: arXiv preprint arXiv:1903.12394 – volume: 112 start-page: 108705 year: 2020 ident: 10.1016/j.apm.2023.07.011_bib0105 article-title: Physics informed topology learning in networks of linear dynamical systems publication-title: Automatica doi: 10.1016/j.automatica.2019.108705 – ident: 10.1016/j.apm.2023.07.011_bib0122 doi: 10.1016/j.apmt.2020.100898 – year: 2012 ident: 10.1016/j.apm.2023.07.011_bib0061 article-title: A knowledge-based reasoning model using causal table for identifying corrosion failure mechanisms in refining and petrochemical plants publication-title: Eng. Fail Anal. – start-page: 1 year: 2022 ident: 10.1016/j.apm.2023.07.011_bib0187 article-title: Inspection and maintenance planning for offshore wind structural components: integrating fatigue failure criteria with bayesian networks and markov decision processes publication-title: Struct. Infrastruct. Eng. – start-page: 108900 year: 2022 ident: 10.1016/j.apm.2023.07.011_bib0029 article-title: Physics-informed machine learning for reliability and systems safety applications: state of the art and challenges publication-title: Reliab. Eng. Syst. Saf. – volume: 70 start-page: 1 year: 2021 ident: 10.1016/j.apm.2023.07.011_bib0030 article-title: Applications of unsupervised deep transfer learning to intelligent fault diagnosis: a survey and comparative study publication-title: IEEE Trans. Instrum. Meas. – year: 2021 ident: 10.1016/j.apm.2023.07.011_bib0138 article-title: Physics-augmented learning: a new paradigm beyond physics-informed learning publication-title: arXiv preprint arXiv:2109.13901 – volume: 417 start-page: 36 year: 2020 ident: 10.1016/j.apm.2023.07.011_bib0207 article-title: A comprehensive review on convolutional neural network in machine fault diagnosis publication-title: Neurocomputing doi: 10.1016/j.neucom.2020.07.088 – volume: 1 start-page: 1 issue: 1 year: 2020 ident: 10.1016/j.apm.2023.07.011_bib0032 article-title: Integrating physics-based modeling with machine learning: a survey publication-title: arXiv preprint arXiv:2003.04919 – start-page: 34 year: 2019 ident: 10.1016/j.apm.2023.07.011_bib0038 article-title: Pi-lstm: Physics-infused long short-term memory network – ident: 10.1016/j.apm.2023.07.011_bib0089 – year: 2022 ident: 10.1016/j.apm.2023.07.011_bib0201 article-title: Physics-informed feature space evaluation for diagnostic power monitoring publication-title: IEEE Trans. Ind. Inf. – volume: 19 start-page: 4181 issue: 11 year: 2019 ident: 10.1016/j.apm.2023.07.011_bib0063 article-title: Physics-based convolutional neural network for fault diagnosis of rolling element bearings publication-title: IEEE Sens. J. doi: 10.1109/JSEN.2019.2898634 – volume: 7 start-page: 241 issue: 2 year: 2020 ident: 10.1016/j.apm.2023.07.011_bib0166 article-title: Fault diagnosis of power transformers using graph convolutional network publication-title: CSEE J. Power Energy Syst. – year: 2020 ident: 10.1016/j.apm.2023.07.011_bib0209 article-title: A physics model embedded hybrid deep neural network for drillstring washout detection – volume: 151 start-page: 107106 year: 2020 ident: 10.1016/j.apm.2023.07.011_bib0183 article-title: A novel scalable method for machine degradation assessment using deep convolutional neural network publication-title: Measurement doi: 10.1016/j.measurement.2019.107106 – year: 2021 ident: 10.1016/j.apm.2023.07.011_bib0009 article-title: Explainable ai (xai) for phm of industrial asset: astate-of-the-art, prisma-compliant systematic review publication-title: arXiv preprint arXiv:2107.03869 – year: 2020 ident: 10.1016/j.apm.2023.07.011_bib0199 article-title: Knowledge transfer between buildings for seismic damage diagnosis through adversarial learning publication-title: arXiv preprint arXiv:2002.09513 – volume: 229 start-page: 108898 year: 2023 ident: 10.1016/j.apm.2023.07.011_bib0188 article-title: Physics-informed machine learning for degradation modeling of an electro-hydrostatic actuator system publication-title: Reliab. Eng. Syst. Saf. doi: 10.1016/j.ress.2022.108898 – year: 2019 ident: 10.1016/j.apm.2023.07.011_bib0134 article-title: Unsupervised and Physics-Informed Machine Learning of Big and Noisy Data – volume: 8 issue: 8 year: 2016 ident: 10.1016/j.apm.2023.07.011_bib0056 article-title: A review of physics-based models in prognostics: application to gears and bearings of rotating machinery publication-title: Adv. Mech. Eng. doi: 10.1177/1687814016664660 – volume: 50 start-page: 92 year: 2019 ident: 10.1016/j.apm.2023.07.011_bib0040 article-title: Data fusion and machine learning for industrial prognosis: trends and perspectives towards industry 4.0 publication-title: Inf. Fusion doi: 10.1016/j.inffus.2018.10.005 – volume: 1338 start-page: 28 year: 2021 ident: 10.1016/j.apm.2023.07.011_bib0144 article-title: Quantifying uncertainty in physics-informed variational autoencoders for anomaly detection publication-title: Impact and Opportunities of Artificial Intelligence Techniques in the Steel Industry: Ongoing Applications, Perspectives and Future Trends – year: 2021 ident: 10.1016/j.apm.2023.07.011_bib0127 article-title: Efficient training of physics-informed neural networks via importance sampling publication-title: Comput.-Aided Civ. Infrastruct. Eng. doi: 10.1111/mice.12685 – start-page: 1 year: 2018 ident: 10.1016/j.apm.2023.07.011_bib0154 article-title: Physics-informed machine learning for dram error modeling – volume: volume 11 year: 2019 ident: 10.1016/j.apm.2023.07.011_bib0121 article-title: Physics-informed neural networks for corrosion-fatigue prognosis – year: 2020 ident: 10.1016/j.apm.2023.07.011_bib0217 article-title: Universal battery performance and degradation model for electric aircraft publication-title: arXiv preprint arXiv:2008.01527 – volume: 165 start-page: 108153 year: 2022 ident: 10.1016/j.apm.2023.07.011_bib0174 article-title: Perception modelling by invariant representation of deep learning for automated structural diagnostic in aircraft maintenance: astudy case using deepshm publication-title: Mech. Syst. Signal Process. doi: 10.1016/j.ymssp.2021.108153 – year: 2018 ident: 10.1016/j.apm.2023.07.011_bib0068 article-title: Physics-based learning for aircraft dynamics simulation – volume: 454 start-page: 324 year: 2021 ident: 10.1016/j.apm.2023.07.011_bib0160 article-title: Implicit supervision for fault detection and segmentation of emerging fault types with deep variational autoencoders publication-title: Neurocomputing doi: 10.1016/j.neucom.2021.04.122 – volume: 92 start-page: 103678 year: 2020 ident: 10.1016/j.apm.2023.07.011_bib0003 article-title: Potential, challenges and future directions for deep learning in prognostics and health management applications publication-title: Eng. Appl. Artif. Intell. doi: 10.1016/j.engappai.2020.103678 – year: 2020 ident: 10.1016/j.apm.2023.07.011_bib0177 article-title: Uncovering the underlying physics of degrading system behavior through a deep neural network framework: the case of remaining useful life prognosis publication-title: arXiv preprint arXiv:2006.09288 – start-page: 1444 year: 2019 ident: 10.1016/j.apm.2023.07.011_bib0194 article-title: Data preparation and preprocessing for broadcast systems monitoring in phm framework – ident: 10.1016/j.apm.2023.07.011_bib0022 – year: 2022 ident: 10.1016/j.apm.2023.07.011_bib0176 article-title: Physics informed neural network for dynamic stress prediction publication-title: arXiv preprint arXiv:2211.16190 – volume: 8 start-page: 1 issue: 060 year: 2017 ident: 10.1016/j.apm.2023.07.011_bib0031 article-title: Prognostics and health management for maintenance practitioners-review, implementation and tools evaluation publication-title: Int. J. Prognost. Health Manag. – volume: 8 start-page: 431 issue: 5 year: 2020 ident: 10.1016/j.apm.2023.07.011_bib0073 article-title: Physics-guided deep learning for drag force prediction in dense fluid-particulate systems publication-title: Big Data doi: 10.1089/big.2020.0071 – volume: 217 start-page: 107961 year: 2022 ident: 10.1016/j.apm.2023.07.011_bib0054 article-title: Fusing physics-based and deep learning models for prognostics publication-title: Reliab. Eng. Syst. Saf. doi: 10.1016/j.ress.2021.107961 – year: 2021 ident: 10.1016/j.apm.2023.07.011_bib0039 article-title: Multi-fidelity information fusion with concatenated neural networks publication-title: arXiv preprint arXiv:2110.04170 – ident: 10.1016/j.apm.2023.07.011_bib0180 doi: 10.26434/chemrxiv.14661180.v1 – volume: 169 start-page: 108779 year: 2022 ident: 10.1016/j.apm.2023.07.011_bib0168 article-title: Interpretable online updated weights: optimized square envelope spectrum for machine condition monitoring and fault diagnosis publication-title: Mech. Syst. Signal Process. doi: 10.1016/j.ymssp.2021.108779 – year: 2017 ident: 10.1016/j.apm.2023.07.011_bib0139 article-title: Physics informed deep learning (part i): data-driven solutions of nonlinear partial differential equations publication-title: arXiv preprint arXiv:1711.10561 – year: 2021 ident: 10.1016/j.apm.2023.07.011_bib0027 article-title: Physics-Infused AI/ML Based Digital-Twin Framework for Flow-Induced-Vibration Damage Prediction in a Nuclear Reactor Heat Exchanger – year: 2021 ident: 10.1016/j.apm.2023.07.011_bib0081 article-title: Microcrack defect quantification using a focusing high-order sh guided wave emat: the physics-informed deep neural network guwnet publication-title: IEEE Trans. Ind. Inf. – volume: 20 start-page: 1675 issue: 4 year: 2021 ident: 10.1016/j.apm.2023.07.011_bib0084 article-title: Structural damage identification via physics-guided machine learning: a methodology integrating pattern recognition with finite element model updating publication-title: Struct. Health Monitor. doi: 10.1177/1475921720927488 – volume: 245 start-page: 106458 year: 2021 ident: 10.1016/j.apm.2023.07.011_bib0115 article-title: Estimating model inadequacy in ordinary differential equations with physics-informed neural networks publication-title: Comput. Struct. doi: 10.1016/j.compstruc.2020.106458 – start-page: 0418 year: 2020 ident: 10.1016/j.apm.2023.07.011_bib0195 article-title: Toward predictive digital twins via component-based reduced-order models and interpretable machine learning – volume: 215 start-page: 110704 year: 2020 ident: 10.1016/j.apm.2023.07.011_bib0085 article-title: Physics-guided convolutional neural network (phycnn) for data-driven seismic response modeling publication-title: Eng. Struct. doi: 10.1016/j.engstruct.2020.110704 – volume: 34 start-page: 1 issue: 1 year: 2021 ident: 10.1016/j.apm.2023.07.011_bib0191 article-title: Deep learning-driven data curation and model interpretation for smart manufacturing publication-title: Chinese J. Mech. Eng. doi: 10.1186/s10033-021-00587-y – volume: 24 start-page: 100797 issue: 1 year: 2020 ident: 10.1016/j.apm.2023.07.011_bib0046 article-title: Coupling in situ experiments and modeling–opportunities for data fusion, machine learning, and discovery of emergent behavior publication-title: Curr. Opin. Solid State Mater. Sci. doi: 10.1016/j.cossms.2019.100797 – volume: 104 start-page: 799 year: 2018 ident: 10.1016/j.apm.2023.07.011_bib0019 article-title: Machinery health prognostics: a systematic review from data acquisition to rul prediction publication-title: Mech. Syst. Signal Process. doi: 10.1016/j.ymssp.2017.11.016 – volume: 12 issue: 2 year: 2021 ident: 10.1016/j.apm.2023.07.011_bib0178 article-title: Remaining useful life estimation using neural ordinary differential equations publication-title: Int. J. Prognost. Health Manag. doi: 10.36001/ijphm.2021.v12i2.2938 – volume: 103 start-page: 104295 year: 2021 ident: 10.1016/j.apm.2023.07.011_bib0143 article-title: A physics-informed deep learning approach for bearing fault detection publication-title: Eng. Appl. Artif. Intell. doi: 10.1016/j.engappai.2021.104295 – volume: 383 start-page: 113885 year: 2021 ident: 10.1016/j.apm.2023.07.011_bib0070 article-title: A phase field and deep-learning based approach for accurate prediction of structural residual useful life publication-title: Comput. Methods Appl. Mech. Eng. doi: 10.1016/j.cma.2021.113885 – volume: 10 issue: 2 year: 2019 ident: 10.1016/j.apm.2023.07.011_bib0182 article-title: A new hybrid prognostic methodology publication-title: Int. J. Prognost. Health Manag. doi: 10.36001/ijphm.2019.v10i2.2727 – year: 2022 ident: 10.1016/j.apm.2023.07.011_bib0219 article-title: A framework for generating large data sets for fatigue damage prognostic problems publication-title: Proc. of 2022 IEEE International Conference on Prognostics and Health Management (ICPHM) doi: 10.1109/ICPHM53196.2022.9815692 – start-page: 1 year: 2021 ident: 10.1016/j.apm.2023.07.011_bib0140 article-title: Physics-informed neural networks for non-linear system identification for power system dynamics – year: 2021 ident: 10.1016/j.apm.2023.07.011_bib0020 article-title: Physics-informed graphical neural network for parameter & state estimations in power systems publication-title: arXiv preprint arXiv:2102.06349 – volume: 229 start-page: 111582 year: 2021 ident: 10.1016/j.apm.2023.07.011_bib0050 article-title: Dynnet: physics-based neural architecture design for nonlinear structural response modeling and prediction publication-title: Eng. Struct. doi: 10.1016/j.engstruct.2020.111582 – volume: 139 issue: 10 year: 2017 ident: 10.1016/j.apm.2023.07.011_bib0206 article-title: Orthogonal analysis of multisensor data fusion for improved quality control publication-title: J. Manuf. Sci. Eng. doi: 10.1115/1.4036907 – volume: 62 start-page: 17 year: 2022 ident: 10.1016/j.apm.2023.07.011_bib0034 article-title: Physics-informed meta learning for machining tool wear prediction publication-title: J. Manuf. Syst. doi: 10.1016/j.jmsy.2021.10.013 – volume: 440 start-page: 110414 year: 2021 ident: 10.1016/j.apm.2023.07.011_bib0098 article-title: Solving inverse-pde problems with physics-aware neural networks publication-title: J. Comput. Phys. doi: 10.1016/j.jcp.2021.110414 – volume: 166 start-page: 108426 year: 2022 ident: 10.1016/j.apm.2023.07.011_bib0080 article-title: Transfer-learning guided bayesian model updating for damage identification considering modeling uncertainty publication-title: Mech. Syst. Signal Process. doi: 10.1016/j.ymssp.2021.108426 – volume: 9 start-page: 3473 issue: 17 year: 2019 ident: 10.1016/j.apm.2023.07.011_bib0045 article-title: Information fusion for multi-source material data: progress and challenges publication-title: Appl. Sci. doi: 10.3390/app9173473 – volume: 391 start-page: 114587 year: 2022 ident: 10.1016/j.apm.2023.07.011_bib0129 article-title: A physics-informed variational deeponet for predicting crack path in quasi-brittle materials publication-title: Comput. Methods Appl. Mech. Eng. doi: 10.1016/j.cma.2022.114587 – volume: 62 start-page: 145 year: 2022 ident: 10.1016/j.apm.2023.07.011_bib0106 article-title: Machine learning for metal additive manufacturing: towards a physics-informed data-driven paradigm publication-title: J. Manuf. Syst. doi: 10.1016/j.jmsy.2021.11.003 – volume: 378 start-page: 686 year: 2019 ident: 10.1016/j.apm.2023.07.011_bib0021 article-title: Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations publication-title: J. Comput. Phys. doi: 10.1016/j.jcp.2018.10.045 – start-page: 532 year: 2020 ident: 10.1016/j.apm.2023.07.011_bib0086 article-title: Physics-guided architecture (pga) of neural networks for quantifying uncertainty in lake temperature modeling – volume: 166 start-page: 108453 year: 2022 ident: 10.1016/j.apm.2023.07.011_bib0161 article-title: Prediction of follower jumps in cam-follower mechanisms: the benefit of using physics-inspired features in recurrent neural networks publication-title: Mech. Syst. Signal Process. doi: 10.1016/j.ymssp.2021.108453 – volume: 5 start-page: 167 issue: 2 year: 2018 ident: 10.1016/j.apm.2023.07.011_bib0015 article-title: Similarity criteria and coal-like material in coal and gas outburst physical simulation publication-title: Int. J. Coal Sci. Technol. doi: 10.1007/s40789-018-0203-8 – volume: 54 start-page: 53 issue: 20 year: 2021 ident: 10.1016/j.apm.2023.07.011_bib0169 article-title: Fault cause assignment with physics informed transfer learning publication-title: IFAC-PapersOnLine doi: 10.1016/j.ifacol.2021.11.152 – start-page: 230 year: 2021 ident: 10.1016/j.apm.2023.07.011_bib0190 article-title: Knowledge incorporation for machine learning in condition monitoring: A survey – year: 2017 ident: 10.1016/j.apm.2023.07.011_bib0094 article-title: Physics-guided neural networks (pgnn): an application in lake temperature modeling publication-title: arXiv preprint arXiv:1710.11431 – ident: 10.1016/j.apm.2023.07.011_bib0026 – volume: 171 start-page: 108907 year: 2022 ident: 10.1016/j.apm.2023.07.011_bib0124 article-title: Physics-informed lstm hyperparameters selection for gearbox fault detection publication-title: Mech. Syst. Signal Process. doi: 10.1016/j.ymssp.2022.108907 – volume: 96 start-page: 106665 year: 2020 ident: 10.1016/j.apm.2023.07.011_bib0100 article-title: A physics-aware learning architecture with input transfer networks for predictive modeling publication-title: Appl. Soft. Comput. doi: 10.1016/j.asoc.2020.106665 – volume: 54 start-page: 62 year: 2020 ident: 10.1016/j.apm.2023.07.011_bib0057 article-title: A nmf-based extraction of physically meaningful components from sensory data of metal casting processes publication-title: J. Manuf. Syst. doi: 10.1016/j.jmsy.2019.09.013 – volume: 363 start-page: 112892 year: 2020 ident: 10.1016/j.apm.2023.07.011_bib0091 article-title: Fea-net: a physics-guided data-driven model for efficient mechanical response prediction publication-title: Comput. Methods Appl. Mech. Eng. doi: 10.1016/j.cma.2020.112892 – volume: 57 start-page: 395 issue: 7 year: 2015 ident: 10.1016/j.apm.2023.07.011_bib0198 article-title: Synthetic data generation in hybrid modelling of rolling element bearings publication-title: Insight-Non-Destruct. Test. Condit. Monitor. doi: 10.1784/insi.2015.57.7.395 – volume: 217 start-page: 108114 year: 2022 ident: 10.1016/j.apm.2023.07.011_bib0062 article-title: Knowledge-based data augmentation of small samples for oil condition prediction publication-title: Reliab. Eng. Syst. Saf. doi: 10.1016/j.ress.2021.108114 – start-page: 1 year: 2021 ident: 10.1016/j.apm.2023.07.011_bib0189 article-title: Knowledge integration into deep learning in dynamical systems: an overview and taxonomy publication-title: J. Mech. Sci. Technol. – start-page: 1 year: 2019 ident: 10.1016/j.apm.2023.07.011_bib0150 article-title: Wave physics informed dictionary learning in one dimension – volume: 66 start-page: 1 issue: 1 year: 2023 ident: 10.1016/j.apm.2023.07.011_bib0193 article-title: A comprehensive review of digital twin–part 2: roles of uncertainty quantification and optimization, a battery digital twin, and perspectives publication-title: Struct. Multidiscip. Optim. doi: 10.1007/s00158-022-03410-x – volume: 445 start-page: 110624 year: 2021 ident: 10.1016/j.apm.2023.07.011_bib0090 article-title: Theory-guided hard constraint projection (hcp): aknowledge-based data-driven scientific machine learning method publication-title: J. Comput. Phys. doi: 10.1016/j.jcp.2021.110624 – ident: 10.1016/j.apm.2023.07.011_bib0025 – year: 2019 ident: 10.1016/j.apm.2023.07.011_bib0152 article-title: Physics-informed machine learning method for forecasting and uncertainty quantification of partially observed and unobserved states in power grids doi: 10.24251/HICSS.2019.416 – start-page: 28 year: 2020 ident: 10.1016/j.apm.2023.07.011_bib0145 article-title: Quantifying uncertainty in physics-informed variational autoencoders for anomaly detection – start-page: 1860 year: 2020 ident: 10.1016/j.apm.2023.07.011_bib0053 article-title: Physics-based deep learning for probabilistic fracture analysis of composite materials – volume: 28 start-page: 3801 issue: 5 year: 2021 ident: 10.1016/j.apm.2023.07.011_bib0125 article-title: A survey of bayesian calibration and physics-informed neural networks in scientific modeling publication-title: Arch. Comput. Methods Eng. doi: 10.1007/s11831-021-09539-0 – volume: volume 7 start-page: 118 year: 2022 ident: 10.1016/j.apm.2023.07.011_bib0181 article-title: Physics-informed lightweight temporal convolution networks for fault prognostics associated to bearing stiffness degradation – volume: 155 start-page: 107614 year: 2021 ident: 10.1016/j.apm.2023.07.011_bib0196 article-title: Digital twin, physics-based model, and machine learning applied to damage detection in structures publication-title: Mech. Syst. Signal Process. doi: 10.1016/j.ymssp.2021.107614 – year: 2020 ident: 10.1016/j.apm.2023.07.011_bib0058 article-title: Increasing facility uptime using machine learning and physics-based hybrid analytics in a dynamic digital twin – volume: 33 start-page: 011701 issue: 1 year: 2021 ident: 10.1016/j.apm.2023.07.011_bib0087 article-title: Physics guided machine learning using simplified theories publication-title: Phys. Fluids doi: 10.1063/5.0038929 – volume: 154 start-page: 107552 year: 2021 ident: 10.1016/j.apm.2023.07.011_bib0107 article-title: Adjusting a torsional vibration damper model with physics-informed neural networks publication-title: Mech. Syst. Signal Process. doi: 10.1016/j.ymssp.2020.107552 – start-page: 1 year: 2021 ident: 10.1016/j.apm.2023.07.011_bib0110 article-title: Certification approach for physics informed machine learning and its application in landing gear life assessment – ident: 10.1016/j.apm.2023.07.011_bib0119 – volume: 22 start-page: 9494 issue: 23 year: 2022 ident: 10.1016/j.apm.2023.07.011_bib0165 article-title: Comparative study between physics-informed cnn and pca in induction motor broken bars mcsa detection publication-title: Sensors doi: 10.3390/s22239494 – volume: 8 issue: 2 year: 2017 ident: 10.1016/j.apm.2023.07.011_bib0052 article-title: Multi-physics based simulations of an oleo-pneumatic shock absorber system for phm publication-title: Int. J. Prognost. Health Manag. – start-page: 558 year: 2019 ident: 10.1016/j.apm.2023.07.011_bib0083 article-title: Physics guided rnns for modeling dynamical systems: A case study in simulating lake temperature profiles – volume: 28 start-page: 1017 issue: 3 year: 2021 ident: 10.1016/j.apm.2023.07.011_bib0203 article-title: Multiscale modeling meets machine learning: what can we learn? publication-title: Arch. Comput. Methods Eng. doi: 10.1007/s11831-020-09405-5 – volume: 42 start-page: 63 year: 2016 ident: 10.1016/j.apm.2023.07.011_bib0216 article-title: Bridging data-driven and model-based approaches for process fault diagnosis and health monitoring: a review of researches and future challenges publication-title: Annu. Rev. Control doi: 10.1016/j.arcontrol.2016.09.008 – volume: 218 start-page: 108119 year: 2022 ident: 10.1016/j.apm.2023.07.011_bib0008 article-title: Prognostics and health management (phm): where are we and where do we (need to) go in theory and practice publication-title: Reliab. Eng. Syst. Saf. doi: 10.1016/j.ress.2021.108119 – volume: 146 start-page: 268 year: 2019 ident: 10.1016/j.apm.2023.07.011_bib0208 article-title: A deep convolutional neural network based fusion method of two-direction vibration signal data for health state identification of planetary gearboxes publication-title: Measurement doi: 10.1016/j.measurement.2019.04.093 – start-page: arXiv year: 2021 ident: 10.1016/j.apm.2023.07.011_bib0132 article-title: Physics-informed graph learning for robust fault location in distribution systems publication-title: arXiv e-prints – volume: 35 start-page: 4347 issue: 6 year: 2020 ident: 10.1016/j.apm.2023.07.011_bib0095 article-title: Physics-aware neural networks for distribution system state estimation publication-title: IEEE Trans. Power Syst. doi: 10.1109/TPWRS.2020.2988352 – volume: 4 start-page: 230 issue: 3 year: 2021 ident: 10.1016/j.apm.2023.07.011_bib0162 article-title: Development and synchronisation of a physics-based model for heating, ventilation and air conditioning system integrated into a hybrid model publication-title: Int. J. Hydromechatron. doi: 10.1504/IJHM.2021.118005 – volume: 15 start-page: 558 issue: 2 year: 2022 ident: 10.1016/j.apm.2023.07.011_bib0126 article-title: Using transfer learning to build physics-informed machine learning models for improved wind farm monitoring publication-title: Energies doi: 10.3390/en15020558 – volume: 404 start-page: 109136 year: 2020 ident: 10.1016/j.apm.2023.07.011_bib0108 article-title: Adaptive activation functions accelerate convergence in deep and physics-informed neural networks publication-title: J. Comput. Phys. doi: 10.1016/j.jcp.2019.109136 – start-page: 1149 year: 2020 ident: 10.1016/j.apm.2023.07.011_bib0117 article-title: Physics-informed neural networks for bias compensation in corrosion-fatigue – volume: 279 start-page: 105857 year: 2020 ident: 10.1016/j.apm.2023.07.011_bib0074 article-title: Physics-guided deep learning for predicting geological drilling risk of wellbore instability using seismic attributes data publication-title: Eng. Geol. doi: 10.1016/j.enggeo.2020.105857 – volume: 137 start-page: 104568 year: 2021 ident: 10.1016/j.apm.2023.07.011_bib0142 article-title: Visualization of hydraulic fracture using physics-informed clustering to process ultrasonic shear waves publication-title: Int. J. Rock Mech. Min. Sci. doi: 10.1016/j.ijrmms.2020.104568 – ident: 10.1016/j.apm.2023.07.011_bib0184 doi: 10.21203/rs.3.rs-863306/v1 – volume: 151 start-page: 107374 year: 2021 ident: 10.1016/j.apm.2023.07.011_bib0112 article-title: Phymdan: physics-informed knowledge transfer between buildings for seismic damage diagnosis through adversarial learning publication-title: Mech. Syst. Signal Process. doi: 10.1016/j.ymssp.2020.107374 – volume: 58 start-page: 5459 issue: 12 year: 2020 ident: 10.1016/j.apm.2023.07.011_bib0186 article-title: Cumulative damage modeling with recurrent neural networks publication-title: AIAA J. doi: 10.2514/1.J059250 – volume: 8 start-page: 71050 year: 2020 ident: 10.1016/j.apm.2023.07.011_bib0072 article-title: Driven by data or derived through physics? a review of hybrid physics guided machine learning techniques with cyber-physical system (cps) focus publication-title: IEEE Access doi: 10.1109/ACCESS.2020.2987324 – volume: 107 start-page: 241 year: 2018 ident: 10.1016/j.apm.2023.07.011_bib0007 article-title: A review on the application of deep learning in system health management publication-title: Mech. Syst. Signal Process. doi: 10.1016/j.ymssp.2017.11.024 – start-page: 1 year: 2019 ident: 10.1016/j.apm.2023.07.011_bib0041 article-title: A permutation entropy-based importance measure for condition monitoring data fusion in fault diagnosis – volume: 258 start-page: 106678 year: 2022 ident: 10.1016/j.apm.2023.07.011_bib0109 article-title: Recurrent neural network-based multiaxial plasticity model with regularization for physics-informed constraints publication-title: Comput. Struct. doi: 10.1016/j.compstruc.2021.106678 – volume: volume 11423 start-page: 114230K year: 2020 ident: 10.1016/j.apm.2023.07.011_bib0101 article-title: Data fusion methods for materials awareness – year: 2021 ident: 10.1016/j.apm.2023.07.011_bib0210 article-title: A data-driven approach to full-field damage and failure pattern prediction in microstructure-dependent composites using deep learning publication-title: arXiv preprint arXiv:2104.04485 – volume: 39 start-page: 101876 year: 2021 ident: 10.1016/j.apm.2023.07.011_bib0093 article-title: Fatigue property prediction of additively manufactured ti-6al-4v using probabilistic physics-guided learning publication-title: Addit. Manuf. – volume: 171 start-page: 108917 year: 2022 ident: 10.1016/j.apm.2023.07.011_bib0185 article-title: On integrating prior knowledge into gaussian processes for prognostic health monitoring publication-title: Mech. Syst. Signal Process. doi: 10.1016/j.ymssp.2022.108917 – volume: 44 start-page: 191 year: 2016 ident: 10.1016/j.apm.2023.07.011_bib0051 article-title: A hybrid framework combining data-driven and model-based methods for system remaining useful life prediction publication-title: Appl. Soft. Comput. doi: 10.1016/j.asoc.2016.03.013 – year: 2019 ident: 10.1016/j.apm.2023.07.011_bib0153 article-title: Acoustic emission analysis of fracture initiation and propagation using physics-informed machine learning methods – year: 2020 ident: 10.1016/j.apm.2023.07.011_bib0069 article-title: Learning mesh-based simulation with graph networks publication-title: arXiv preprint arXiv:2010.03409 – year: 2022 ident: 10.1016/j.apm.2023.07.011_bib0123 article-title: Knowledge informed machine learning using a weibull-based loss function publication-title: arXiv preprint arXiv:2201.01769 – year: 2020 ident: 10.1016/j.apm.2023.07.011_bib0136 article-title: Simulation free reliability analysis: aphysics-informed deep learning based approach publication-title: arXiv preprint arXiv:2005.01302 – volume: 1 start-page: 16 issue: 1 year: 2015 ident: 10.1016/j.apm.2023.07.011_bib0044 article-title: Methodologies for cross-domain data fusion: an overview publication-title: IEEE Trans. Big Data doi: 10.1109/TBDATA.2015.2465959 – volume: 428 start-page: 116 year: 2021 ident: 10.1016/j.apm.2023.07.011_bib0047 article-title: Midphynet: memorized infusion of decomposed physics in neural networks to model dynamic systems publication-title: Neurocomputing doi: 10.1016/j.neucom.2020.11.042 – volume: 26 start-page: e2358 issue: 6 year: 2019 ident: 10.1016/j.apm.2023.07.011_bib0088 article-title: Estimation of full-field, full-order experimental modal model of cable vibration from digital video measurements with physics-guided unsupervised machine learning and computer vision publication-title: Struct. Control Health Monitor. doi: 10.1002/stc.2358 – volume: 65 start-page: 354 issue: 12 year: 2022 ident: 10.1016/j.apm.2023.07.011_bib0192 article-title: A comprehensive review of digital twin–part 1: modeling and twinning enabling technologies publication-title: Struct. Multidiscip. Optim. doi: 10.1007/s00158-022-03425-4 – volume: 9 start-page: 2336 issue: 18 year: 2021 ident: 10.1016/j.apm.2023.07.011_bib0006 article-title: Synthetic data augmentation and deep learning for the fault diagnosis of rotating machines publication-title: Mathematics doi: 10.3390/math9182336 – volume: 3 start-page: 422 issue: 6 year: 2021 ident: 10.1016/j.apm.2023.07.011_bib0164 article-title: Physics-informed machine learning publication-title: Nature Rev. Phys. doi: 10.1038/s42254-021-00314-5 – year: 2018 ident: 10.1016/j.apm.2023.07.011_bib0111 article-title: Physics-informed kriging: a physics-informed gaussian process regression method for data-model convergence publication-title: arXiv preprint arXiv:1809.03461 – volume: 6 start-page: 04020013 issue: 2 year: 2020 ident: 10.1016/j.apm.2023.07.011_bib0118 article-title: A data-driven physics-informed method for prognosis of infrastructure systems: theory and application to crack prediction publication-title: ASCE-ASME J. Risk Uncertain. Eng. Syst., Part A: Civil Eng. doi: 10.1061/AJRUA6.0001053 – volume: 181 start-page: 109631 year: 2021 ident: 10.1016/j.apm.2023.07.011_bib0173 article-title: Gas path fault diagnosis for gas turbine group based on deep transfer learning publication-title: Measurement doi: 10.1016/j.measurement.2021.109631 – volume: 163 start-page: 107929 year: 2020 ident: 10.1016/j.apm.2023.07.011_bib0002 article-title: Deep learning for prognostics and health management: state of the art, challenges, and opportunities publication-title: Measurement doi: 10.1016/j.measurement.2020.107929 – year: 2018 ident: 10.1016/j.apm.2023.07.011_bib0149 article-title: Physics-informed machine learning method for forecasting and uncertainty quantification of partially observed and unobserved states in power grids publication-title: arXiv preprint arXiv:1806.10990 – year: 2021 ident: 10.1016/j.apm.2023.07.011_bib0157 article-title: Characterizing possible failure modes in physics-informed neural networks publication-title: arXiv preprint arXiv:2109.01050 – year: 2016 ident: 10.1016/j.apm.2023.07.011_bib0035 – year: 2019 ident: 10.1016/j.apm.2023.07.011_bib0099 article-title: Physics-aware difference graph networks for sparsely-observed dynamics |
| SSID | ssj0005904 ssib019627096 |
| Score | 2.6176386 |
| Snippet | •Systematic bibliometric analysis of PIML in PHM.•Novel perspectives for PIML from the “Informed knowledge forms” and “Informed methods”.•Taxonomy of PIML... Prognostics and health management (PHM) plays a constructive role in the equipment’s entire life health service. It has long benefited from intensive research... |
| SourceID | hal crossref elsevier |
| SourceType | Open Access Repository Enrichment Source Index Database Publisher |
| StartPage | 325 |
| SubjectTerms | Engineering Sciences Knowledge Materials Physics-constraint learning Physics-embedded algorithm structure Physics-informed input space Physics-informed machine learning Prognostics and health management |
| Title | Physics-informed machine learning in prognostics and health management: State of the art and challenges |
| URI | https://dx.doi.org/10.1016/j.apm.2023.07.011 https://hal.science/hal-04290849 |
| Volume | 124 |
| WOSCitedRecordID | wos001061367700001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 customDbUrl: eissn: 1872-8480 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0005904 issn: 0307-904X databaseCode: AIEXJ dateStart: 20211207 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Lj9MwELbKLgc4IJ5ieclCnECp0tiJbW4V7NJll6oSRfRmOYnTB21abR9afiL_inEcJ9kiVsuBi1WljtPOfB2PpzPfIPSGZ5FKTQhMKG5CNyTxeEQ7XkwIOMBxnETUNptg_T4fjcSg1frlamF2c5bn_PJSrP6rquEaKNuUzv6DuqtF4QK8BqXDCGqH8UaKL3I6k7VnKVHBn1wU-ZLaNYgoSlhMWla-rDmabTlkmcvqAoaFI-qSCOBJtgbOdV9ZN_1a58wuKhZYU5ViuuzM3d5ovGVtLct3Pf2xrVDZH29_WuN3NlH55N2wPWhXONDpTLne7GqhqnyQT8vxtiZHcBgvwxcB2UsFcXU1DbNnaCuFb_M229qaZc7AblPb86my2wFtWF5i66fLTZxYWtw_9gcbqpi11cqwEASkIG4trf0VLu5e96scfDyR56f9s6vvNhIYe91zGEHsntnWfU7FDg7ihwELBRjYw-7p8eizM24d0-XIN9yGLv9I-NSxdJqv6_5yL5IP9z7f35ymWxMX_i_coeF9dK88x-Cuxd8D1NL5Q3T3S6X-9SM03kciLpGIHRLxNMcNJGLAF7ZIxDUS3-MCh3iZYVgcAw6LeTUOH6NvJ8fDDz2v7OvhJYTRjZeAfxCmLA4yxYiG8y6JWBoyBScVnZCAaNPkTGVpqDKqwlhwnigehQzMSkbDjJAn6CBf5vopwimJ4jAiHRULRTtBJhKhMu4rlWZExSw9Qr4TnExK0nvTe2UuXXbjTIKspZG19JkEWR-ht9UtK8v4ct1k6rQhS5fVuqISEHfdba9BSNXyhuIdsCTNtRpJz24y6Tm6U_-qXqCDzcVWv0S3k91mur54VWLwN6p9wSQ |
| linkProvider | Elsevier |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Physics-informed+machine+learning+in+prognostics+and+health+management%3A+State+of+the+art+and+challenges&rft.jtitle=Applied+mathematical+modelling&rft.au=Deng%2C+Weikun&rft.au=Nguyen%2C+Khanh+T.P.&rft.au=Medjaher%2C+Kamal&rft.au=Gogu%2C+Christian&rft.date=2023-12-01&rft.pub=Elsevier&rft.issn=0307-904X&rft.eissn=1872-8480&rft.volume=124&rft.spage=325&rft.epage=352&rft_id=info:doi/10.1016%2Fj.apm.2023.07.011&rft.externalDBID=HAS_PDF_LINK&rft.externalDocID=oai%3AHAL%3Ahal-04290849v1 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0307-904X&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0307-904X&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0307-904X&client=summon |