A deep learning approach to estimate stress distribution: a fast and accurate surrogate of finite-element analysis
Structural finite-element analysis (FEA) has been widely used to study the biomechanics of human tissues and organs, as well as tissue-medical device interactions, and treatment strategies. However, patient-specific FEA models usually require complex procedures to set up and long computing times to...
Gespeichert in:
| Veröffentlicht in: | Journal of the Royal Society interface Jg. 15; H. 138 |
|---|---|
| Hauptverfasser: | , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
England
01.01.2018
|
| Schlagworte: | |
| ISSN: | 1742-5662, 1742-5662 |
| Online-Zugang: | Weitere Angaben |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | Structural finite-element analysis (FEA) has been widely used to study the biomechanics of human tissues and organs, as well as tissue-medical device interactions, and treatment strategies. However, patient-specific FEA models usually require complex procedures to set up and long computing times to obtain final simulation results, preventing prompt feedback to clinicians in time-sensitive clinical applications. In this study, by using machine learning techniques, we developed a deep learning (DL) model to directly estimate the stress distributions of the aorta. The DL model was designed and trained to take the input of FEA and directly output the aortic wall stress distributions, bypassing the FEA calculation process. The trained DL model is capable of predicting the stress distributions with average errors of 0.492% and 0.891% in the Von Mises stress distribution and peak Von Mises stress, respectively. This study marks, to our knowledge, the first study that demonstrates the feasibility and great potential of using the DL technique as a fast and accurate surrogate of FEA for stress analysis. |
|---|---|
| AbstractList | Structural finite-element analysis (FEA) has been widely used to study the biomechanics of human tissues and organs, as well as tissue-medical device interactions, and treatment strategies. However, patient-specific FEA models usually require complex procedures to set up and long computing times to obtain final simulation results, preventing prompt feedback to clinicians in time-sensitive clinical applications. In this study, by using machine learning techniques, we developed a deep learning (DL) model to directly estimate the stress distributions of the aorta. The DL model was designed and trained to take the input of FEA and directly output the aortic wall stress distributions, bypassing the FEA calculation process. The trained DL model is capable of predicting the stress distributions with average errors of 0.492% and 0.891% in the Von Mises stress distribution and peak Von Mises stress, respectively. This study marks, to our knowledge, the first study that demonstrates the feasibility and great potential of using the DL technique as a fast and accurate surrogate of FEA for stress analysis. Structural finite-element analysis (FEA) has been widely used to study the biomechanics of human tissues and organs, as well as tissue-medical device interactions, and treatment strategies. However, patient-specific FEA models usually require complex procedures to set up and long computing times to obtain final simulation results, preventing prompt feedback to clinicians in time-sensitive clinical applications. In this study, by using machine learning techniques, we developed a deep learning (DL) model to directly estimate the stress distributions of the aorta. The DL model was designed and trained to take the input of FEA and directly output the aortic wall stress distributions, bypassing the FEA calculation process. The trained DL model is capable of predicting the stress distributions with average errors of 0.492% and 0.891% in the Von Mises stress distribution and peak Von Mises stress, respectively. This study marks, to our knowledge, the first study that demonstrates the feasibility and great potential of using the DL technique as a fast and accurate surrogate of FEA for stress analysis.Structural finite-element analysis (FEA) has been widely used to study the biomechanics of human tissues and organs, as well as tissue-medical device interactions, and treatment strategies. However, patient-specific FEA models usually require complex procedures to set up and long computing times to obtain final simulation results, preventing prompt feedback to clinicians in time-sensitive clinical applications. In this study, by using machine learning techniques, we developed a deep learning (DL) model to directly estimate the stress distributions of the aorta. The DL model was designed and trained to take the input of FEA and directly output the aortic wall stress distributions, bypassing the FEA calculation process. The trained DL model is capable of predicting the stress distributions with average errors of 0.492% and 0.891% in the Von Mises stress distribution and peak Von Mises stress, respectively. This study marks, to our knowledge, the first study that demonstrates the feasibility and great potential of using the DL technique as a fast and accurate surrogate of FEA for stress analysis. |
| Author | Liang, Liang Sun, Wei Liu, Minliang Martin, Caitlin |
| Author_xml | – sequence: 1 givenname: Liang surname: Liang fullname: Liang, Liang organization: Tissue Mechanics Laboratory, The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Technology Enterprise Park, Room 206, 387 Technology Circle, Atlanta, GA 30313-2412, USA – sequence: 2 givenname: Minliang surname: Liu fullname: Liu, Minliang organization: Tissue Mechanics Laboratory, The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Technology Enterprise Park, Room 206, 387 Technology Circle, Atlanta, GA 30313-2412, USA – sequence: 3 givenname: Caitlin surname: Martin fullname: Martin, Caitlin organization: Tissue Mechanics Laboratory, The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Technology Enterprise Park, Room 206, 387 Technology Circle, Atlanta, GA 30313-2412, USA – sequence: 4 givenname: Wei orcidid: 0000-0002-8708-5128 surname: Sun fullname: Sun, Wei email: wei.sun@bme.gatech.edu organization: Tissue Mechanics Laboratory, The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Technology Enterprise Park, Room 206, 387 Technology Circle, Atlanta, GA 30313-2412, USA wei.sun@bme.gatech.edu |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/29367242$$D View this record in MEDLINE/PubMed |
| BookMark | eNpNkDtPwzAURi1URB-wMiKPLCmx4zgOW1XxkiqxwBxdx9fFKHGCnQz996RQJKZ7hqNPOndJZr7zSMg1S9csLdVdiM6uecqKdaqEOCMLVgie5FLy2T-ek2WMn2maFVmeX5A5LzNZcMEXJGyoQexpgxC883sKfR86qD_o0FGMg2thQBqHgDFS4yZwehxc5-8pUAtxoOANhboew484htDtj9RZap13AybYYIv-KEJziC5eknMLTcSr012R98eHt-1zsnt9etludkktuRgSsEVhUqtNWStZcitKyYQGnWpuTW6sZlpqrYyyRubAADKLFnPGmMpVJoCvyO3v7hT0NU4tVetijU0DHrsxVqwsJ5eXSk3qzUkddYum6sPUHQ7V35_4N1qIbyM |
| CitedBy_id | crossref_primary_10_3390_app12010394 crossref_primary_10_1016_j_compscitech_2024_110431 crossref_primary_10_1007_s12206_025_0320_4 crossref_primary_10_1016_j_advengsoft_2022_103240 crossref_primary_10_1016_j_compositesa_2025_108823 crossref_primary_10_3389_fphy_2020_00030 crossref_primary_10_3390_diagnostics12071530 crossref_primary_10_1016_j_eswa_2024_125916 crossref_primary_10_1007_s12541_024_01191_5 crossref_primary_10_1088_1755_1315_1333_1_012028 crossref_primary_10_4271_15_17_01_0002 crossref_primary_10_1007_s00158_021_03137_1 crossref_primary_10_1016_j_oceaneng_2024_119431 crossref_primary_10_1080_10667857_2024_2443211 crossref_primary_10_1007_s00500_023_09491_0 crossref_primary_10_1016_j_matdes_2021_109937 crossref_primary_10_1007_s00158_020_02659_4 crossref_primary_10_1016_j_bonr_2022_101179 crossref_primary_10_1016_j_compbiomed_2023_107287 crossref_primary_10_1088_1361_6560_adde0d crossref_primary_10_3390_electronics12224631 crossref_primary_10_1016_j_mfglet_2025_06_165 crossref_primary_10_1016_j_commatsci_2022_111290 crossref_primary_10_1515_bmt_2019_0298 crossref_primary_10_1007_s00366_022_01765_9 crossref_primary_10_1016_j_taml_2023_100489 crossref_primary_10_1007_s00266_025_04924_7 crossref_primary_10_1016_j_jmapro_2025_04_013 crossref_primary_10_3389_fmech_2022_1003170 crossref_primary_10_1016_j_engfracmech_2022_108914 crossref_primary_10_1016_j_mechmat_2025_105418 crossref_primary_10_1080_13588265_2022_2130600 crossref_primary_10_1016_j_geoen_2022_211368 crossref_primary_10_1016_j_jcp_2021_110784 crossref_primary_10_1016_j_compbiomed_2025_110683 crossref_primary_10_1016_j_engfailanal_2024_108632 crossref_primary_10_1016_j_cma_2024_117289 crossref_primary_10_3389_fcvm_2024_1358601 crossref_primary_10_1016_j_compbiomed_2020_104038 crossref_primary_10_1016_j_ijengsci_2020_103319 crossref_primary_10_1016_j_jmbbm_2025_107074 crossref_primary_10_1016_j_aei_2023_102337 crossref_primary_10_1016_j_scriptamat_2021_113805 crossref_primary_10_1016_j_jhydrol_2020_124700 crossref_primary_10_1016_j_procir_2024_10_069 crossref_primary_10_1016_j_compbiomed_2021_104794 crossref_primary_10_1016_j_brain_2023_100079 crossref_primary_10_1002_admt_202201479 crossref_primary_10_1016_j_nucengdes_2022_111842 crossref_primary_10_1007_s11517_025_03294_1 crossref_primary_10_1016_j_jmbbm_2024_106495 crossref_primary_10_1111_ffe_14330 crossref_primary_10_3389_frobt_2021_631371 crossref_primary_10_1016_j_jmbbm_2021_104371 crossref_primary_10_1017_jfm_2022_174 crossref_primary_10_1016_j_jjcc_2022_05_007 crossref_primary_10_1088_2632_2153_ad134a crossref_primary_10_1016_j_ijmecsci_2024_109393 crossref_primary_10_1038_s41598_020_60853_2 crossref_primary_10_1177_03093247241293499 crossref_primary_10_1007_s12008_024_01905_z crossref_primary_10_1016_j_procir_2021_11_295 crossref_primary_10_1108_JFRA_05_2023_0277 crossref_primary_10_1016_j_finel_2021_103572 crossref_primary_10_1038_s41598_020_77935_w crossref_primary_10_1080_10255842_2022_2112183 crossref_primary_10_1007_s00170_021_07084_5 crossref_primary_10_3390_app9245369 crossref_primary_10_1016_j_aei_2020_101184 crossref_primary_10_1016_j_compstruct_2022_115233 crossref_primary_10_3390_s23083938 crossref_primary_10_1016_j_cnsns_2022_106780 crossref_primary_10_1016_j_tafmec_2024_104493 crossref_primary_10_3390_bioengineering9110687 crossref_primary_10_1016_j_heliyon_2022_e11242 crossref_primary_10_1134_S0025654424606943 crossref_primary_10_1007_s40964_024_00686_x crossref_primary_10_1109_ACCESS_2023_3282453 crossref_primary_10_3390_ijms24031948 crossref_primary_10_1007_s00466_024_02475_3 crossref_primary_10_1007_s11517_018_1940_y crossref_primary_10_1007_s12206_023_1130_1 crossref_primary_10_1016_j_cma_2022_115594 crossref_primary_10_1016_j_mechmat_2024_104948 crossref_primary_10_1007_s00466_021_02112_3 crossref_primary_10_1016_j_cma_2024_117372 crossref_primary_10_1016_j_compositesb_2021_109282 crossref_primary_10_1038_s41598_022_09128_6 crossref_primary_10_1007_s00466_023_02434_4 crossref_primary_10_3390_biomechanics2020016 crossref_primary_10_1016_j_jbiomech_2019_109544 crossref_primary_10_1016_j_apenergy_2023_122354 crossref_primary_10_3390_buildings14103215 crossref_primary_10_1016_j_jmps_2022_105044 crossref_primary_10_1080_01495739_2024_2400583 crossref_primary_10_1093_cvr_cvab038 crossref_primary_10_1007_s00170_020_05895_6 crossref_primary_10_1016_j_commatsci_2025_114090 crossref_primary_10_1016_j_istruc_2025_110224 crossref_primary_10_1088_1757_899X_1189_1_012031 crossref_primary_10_1016_j_ijfatigue_2022_106808 crossref_primary_10_1016_j_cma_2024_117486 crossref_primary_10_3389_fcvm_2021_769927 crossref_primary_10_1007_s12265_024_10550_6 crossref_primary_10_1109_JBHI_2022_3198650 crossref_primary_10_3390_ijms21186756 crossref_primary_10_1007_s40430_022_03480_4 crossref_primary_10_1109_ACCESS_2020_2977880 crossref_primary_10_32604_cmes_2022_018519 crossref_primary_10_1080_24725854_2021_1891485 crossref_primary_10_1007_s10439_025_03798_9 crossref_primary_10_1007_s11104_024_07077_9 crossref_primary_10_1007_s10439_022_02967_4 crossref_primary_10_1007_s11709_022_0882_5 crossref_primary_10_1177_09544062211010828 crossref_primary_10_48084_etasr_11631 crossref_primary_10_1115_1_4069278 crossref_primary_10_1080_10255842_2023_2236747 crossref_primary_10_1002_aisy_202200099 crossref_primary_10_1016_j_jmbbm_2024_106859 crossref_primary_10_1016_j_jmbbm_2024_106736 crossref_primary_10_3390_bioengineering6040104 crossref_primary_10_3390_en15114129 crossref_primary_10_1016_j_dte_2025_100057 crossref_primary_10_1016_j_compstruc_2020_106425 crossref_primary_10_1073_pnas_2401230121 crossref_primary_10_1371_journal_pcbi_1010988 crossref_primary_10_1007_s00521_024_09935_0 crossref_primary_10_1007_s10462_024_10931_y crossref_primary_10_1007_s13239_024_00737_y crossref_primary_10_1016_j_media_2020_101845 crossref_primary_10_3390_buildings13020317 crossref_primary_10_1016_j_enganabound_2020_03_028 crossref_primary_10_1080_10298436_2021_1990288 crossref_primary_10_1016_j_ijheatfluidflow_2025_110011 crossref_primary_10_1016_j_engfracmech_2024_110720 crossref_primary_10_3390_s20226439 crossref_primary_10_1007_s00466_021_02009_1 crossref_primary_10_3390_bioengineering12040433 crossref_primary_10_1038_s41467_021_22348_0 crossref_primary_10_1007_s11831_024_10200_9 crossref_primary_10_1016_j_mtcomm_2021_102197 crossref_primary_10_1016_j_compstruc_2021_106714 crossref_primary_10_1016_j_engappai_2023_107187 crossref_primary_10_1016_j_jcp_2024_112952 crossref_primary_10_1016_j_ymssp_2023_111014 crossref_primary_10_3389_fgene_2023_1142446 crossref_primary_10_1016_j_engappai_2023_106894 crossref_primary_10_1016_j_engappai_2021_104232 crossref_primary_10_1039_D5TA00982K crossref_primary_10_1016_j_cma_2025_118046 crossref_primary_10_1038_s41598_019_54707_9 crossref_primary_10_1016_j_enganabound_2022_03_030 crossref_primary_10_1016_j_cmpb_2022_107013 crossref_primary_10_1080_24748706_2020_1740365 crossref_primary_10_1016_j_cma_2020_113401 crossref_primary_10_3390_jpm10020028 crossref_primary_10_1038_s44172_025_00410_9 crossref_primary_10_3389_fphys_2025_1518732 crossref_primary_10_1016_j_oceaneng_2024_120277 crossref_primary_10_3390_asi5050097 crossref_primary_10_1016_j_cma_2024_117446 crossref_primary_10_1016_j_compbiomed_2024_108041 crossref_primary_10_1002_qre_3313 crossref_primary_10_1007_s13369_024_08810_3 crossref_primary_10_1155_2022_4617392 crossref_primary_10_1016_j_advengsoft_2023_103461 crossref_primary_10_1038_s41598_020_79191_4 crossref_primary_10_1016_j_cmpb_2023_107888 crossref_primary_10_1016_j_cmpb_2023_107524 crossref_primary_10_1177_30494826251336314 crossref_primary_10_1016_j_eswa_2022_118884 crossref_primary_10_1016_j_hybadv_2023_100026 crossref_primary_10_1016_j_actamat_2020_03_016 crossref_primary_10_1016_j_apm_2022_02_036 crossref_primary_10_1016_j_ijmecsci_2021_106972 crossref_primary_10_1016_j_mechmat_2021_104191 crossref_primary_10_1007_s00158_023_03593_x crossref_primary_10_3390_jmmp4030092 crossref_primary_10_1088_1674_4527_ac9f06 crossref_primary_10_1080_1064119X_2022_2136045 crossref_primary_10_1007_s40747_023_01276_0 crossref_primary_10_1016_j_jmbbm_2025_107007 crossref_primary_10_48175_IJARSCT_23916 crossref_primary_10_1145_3654662 crossref_primary_10_3389_fcvm_2021_759675 crossref_primary_10_3389_fbuil_2025_1612575 crossref_primary_10_5802_crmeca_240 crossref_primary_10_3390_machines11111013 crossref_primary_10_1007_s11831_022_09795_8 crossref_primary_10_1016_j_compstruc_2021_106484 crossref_primary_10_3390_ma16206815 crossref_primary_10_1007_s00466_023_02278_y crossref_primary_10_1002_nag_3505 crossref_primary_10_1016_j_spinee_2021_05_024 crossref_primary_10_1007_s10845_021_01902_z crossref_primary_10_1016_j_yjmcc_2019_04_026 crossref_primary_10_3389_fbuil_2021_745598 crossref_primary_10_1016_j_measurement_2020_108726 crossref_primary_10_1007_s10409_025_25340_x crossref_primary_10_3390_polym13162592 crossref_primary_10_1016_j_cma_2025_118119 crossref_primary_10_1007_s00158_022_03485_6 crossref_primary_10_1016_j_compstruct_2025_119675 crossref_primary_10_1007_s11831_024_10063_0 crossref_primary_10_1016_j_jcis_2021_11_195 crossref_primary_10_3390_foods10040763 crossref_primary_10_1007_s00266_025_05131_0 crossref_primary_10_1007_s10439_018_2024_8 crossref_primary_10_3390_ma14185200 crossref_primary_10_1007_s00158_024_03908_6 crossref_primary_10_1016_j_compbiomed_2019_04_022 crossref_primary_10_3390_biomedicines10092157 crossref_primary_10_3390_bioengineering12050437 crossref_primary_10_1007_s00500_022_07362_8 crossref_primary_10_3390_fluids7060197 crossref_primary_10_1016_j_actbio_2022_02_027 crossref_primary_10_1016_j_compositesa_2024_108618 crossref_primary_10_1016_j_engappai_2023_106295 crossref_primary_10_3389_fphys_2021_694945 crossref_primary_10_3390_diagnostics14030261 crossref_primary_10_1088_1361_651X_ad4b4c crossref_primary_10_1186_s12872_018_0818_0 crossref_primary_10_1016_j_cma_2023_116347 crossref_primary_10_3390_math11122723 crossref_primary_10_3390_app12178678 crossref_primary_10_1016_j_medengphy_2018_12_001 crossref_primary_10_1103_PhysRevFluids_8_064004 crossref_primary_10_1007_s00170_019_03363_4 crossref_primary_10_3389_fbioe_2024_1268314 crossref_primary_10_1016_j_compbiomed_2022_105699 crossref_primary_10_1177_16878132241290948 crossref_primary_10_1002_admi_202100175 crossref_primary_10_3389_fbioe_2023_1201177 crossref_primary_10_1016_j_cma_2021_114037 crossref_primary_10_1016_j_matdes_2023_112357 crossref_primary_10_1038_s43586_025_00406_x crossref_primary_10_1016_j_engappai_2023_107150 crossref_primary_10_1108_RPJ_04_2024_0168 crossref_primary_10_1007_s10686_025_09980_0 crossref_primary_10_1016_j_engfracmech_2021_107890 crossref_primary_10_3390_act10020028 crossref_primary_10_1016_j_prosdent_2024_06_012 crossref_primary_10_3390_ai5040102 crossref_primary_10_1007_s13239_021_00552_9 crossref_primary_10_2118_201430_PA crossref_primary_10_1016_j_eswa_2024_123953 crossref_primary_10_3390_s21051654 crossref_primary_10_1016_j_jbiomech_2019_01_057 crossref_primary_10_1016_j_eswa_2024_124928 crossref_primary_10_3389_fphy_2019_00117 crossref_primary_10_1080_10494820_2020_1719162 crossref_primary_10_1038_s41598_022_26424_3 crossref_primary_10_3389_fphy_2019_00235 crossref_primary_10_1016_j_eml_2021_101566 crossref_primary_10_1016_j_enganabound_2023_08_020 crossref_primary_10_3390_jcm12144774 crossref_primary_10_1016_j_jmbbm_2022_105577 crossref_primary_10_1088_1361_6668_adfa48 crossref_primary_10_1016_j_cma_2023_116229 crossref_primary_10_1016_j_oceaneng_2024_118768 crossref_primary_10_1016_j_matdes_2023_112126 crossref_primary_10_3390_jcs4020061 crossref_primary_10_1016_j_cma_2021_113959 crossref_primary_10_1016_j_engstruct_2024_119023 crossref_primary_10_1007_s11831_025_10302_y crossref_primary_10_1016_j_jcis_2022_01_037 crossref_primary_10_1007_s11517_025_03311_3 crossref_primary_10_1016_j_epsr_2025_111984 crossref_primary_10_1016_j_jdent_2024_105348 crossref_primary_10_1016_j_cma_2024_117073 crossref_primary_10_1007_s10010_024_00730_w crossref_primary_10_1016_j_ress_2021_107734 crossref_primary_10_3390_buildings14113515 crossref_primary_10_1016_j_heliyon_2023_e16129 crossref_primary_10_1016_j_cma_2022_115766 crossref_primary_10_1007_s00366_024_02033_8 crossref_primary_10_1109_JIOT_2022_3221932 crossref_primary_10_1016_j_molliq_2022_118489 crossref_primary_10_1515_nanoph_2020_0194 |
| ContentType | Journal Article |
| Copyright | 2018 The Author(s). |
| Copyright_xml | – notice: 2018 The Author(s). |
| DBID | NPM 7X8 |
| DOI | 10.1098/rsif.2017.0844 |
| DatabaseName | PubMed MEDLINE - Academic |
| DatabaseTitle | PubMed MEDLINE - Academic |
| DatabaseTitleList | PubMed MEDLINE - Academic |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database |
| DeliveryMethod | no_fulltext_linktorsrc |
| Discipline | Sciences (General) |
| EISSN | 1742-5662 |
| ExternalDocumentID | 29367242 |
| Genre | Journal Article |
| GrantInformation_xml | – fundername: NHLBI NIH HHS grantid: R01 HL104080 |
| GroupedDBID | --- 0R~ 18M 29L 2WC 4.4 53G 5GY 5VS ACGFO ACQIA ACRPL ADBBV ADDVE ADNMO AENEX AFFVI AGPVY AGQPQ AJZGM ALMA_UNASSIGNED_HOLDINGS ALMYZ AOIJS BAWUL BGBPD BTFSW C1A CAG COF CS3 DIK DU5 EBS EJD GX1 H13 HYE HZ~ KQ8 MRS MV1 NPM NSAHA O9- P2P ROL RPM RRY S70 TR2 V1E W8F XSW 7X8 |
| ID | FETCH-LOGICAL-c624t-af77d0fbd9c8692f49614bab0b2fd5dfb1b6bb8d8fd65a1aa3fefe511185834a2 |
| IEDL.DBID | 7X8 |
| ISICitedReferencesCount | 345 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000423770900032&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1742-5662 |
| IngestDate | Thu Oct 02 06:12:26 EDT 2025 Mon Jul 21 06:06:13 EDT 2025 |
| IsDoiOpenAccess | false |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 138 |
| Keywords | deep learning finite-element analysis neural network stress analysis |
| Language | English |
| License | 2018 The Author(s). |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c624t-af77d0fbd9c8692f49614bab0b2fd5dfb1b6bb8d8fd65a1aa3fefe511185834a2 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| ORCID | 0000-0002-8708-5128 |
| OpenAccessLink | https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2017.0844 |
| PMID | 29367242 |
| PQID | 1991182988 |
| PQPubID | 23479 |
| ParticipantIDs | proquest_miscellaneous_1991182988 pubmed_primary_29367242 |
| PublicationCentury | 2000 |
| PublicationDate | 2018-01-01 |
| PublicationDateYYYYMMDD | 2018-01-01 |
| PublicationDate_xml | – month: 01 year: 2018 text: 2018-01-01 day: 01 |
| PublicationDecade | 2010 |
| PublicationPlace | England |
| PublicationPlace_xml | – name: England |
| PublicationTitle | Journal of the Royal Society interface |
| PublicationTitleAlternate | J R Soc Interface |
| PublicationYear | 2018 |
| SSID | ssj0037355 |
| Score | 2.6550574 |
| Snippet | Structural finite-element analysis (FEA) has been widely used to study the biomechanics of human tissues and organs, as well as tissue-medical device... |
| SourceID | proquest pubmed |
| SourceType | Aggregation Database Index Database |
| Title | A deep learning approach to estimate stress distribution: a fast and accurate surrogate of finite-element analysis |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/29367242 https://www.proquest.com/docview/1991182988 |
| Volume | 15 |
| WOSCitedRecordID | wos000423770900032&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpZ07T8MwFIUtoAwsQHmWl4zEAENoajuxw4IqRMVC1QGkbpWfqEtSkpTfz3XilgkJiSWTE0X2tfPda-cchG4SzoVyZgBJjrYR0zGNJIHE1RhvLwJEAqjUmE3w8VhMp9kkFNyqcKxytSY2C7UptK-R9_0RHWDhTIjHxWfkXaP87mqw0NhEHQoo46OaT9e7CJTTxvUUoBsSrjQla9FG0YdMvBHw5PexYOx3vGw-M6O9_77gPtoNgImHbUR00YbND1A3TOEK3wad6btDVA6xsXaBg3HEB17pi-O6wF58A2DW4vZnEmy8wG7wxnrAEjtZ1VjmBkutl2XTcFmWhS_K4cJhN_csG9n2cDo0bKVPjtD76Pnt6SUKFgyRTgmrI-k4N7FTJtMizYhjMIJMSRUr4kxinBqoVClhhDNpIgdSUmedBYgDDBCUSXKMtvIit6cIZ4kiGniQE5Yyw52SVmmexZaajElDe-h61a8zCHG_byFzWyyr2U_P9tBJOzizRavFMQNaSeGJ5OwPd5-jHRhx0RZQLlDHwQS3l2hbf9XzqrxqYgeu48nrN9Pd0Y8 |
| linkProvider | ProQuest |
| 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=A+deep+learning+approach+to+estimate+stress+distribution%3A+a+fast+and+accurate+surrogate+of+finite-element+analysis&rft.jtitle=Journal+of+the+Royal+Society+interface&rft.au=Liang%2C+Liang&rft.au=Liu%2C+Minliang&rft.au=Martin%2C+Caitlin&rft.au=Sun%2C+Wei&rft.date=2018-01-01&rft.issn=1742-5662&rft.eissn=1742-5662&rft.volume=15&rft.issue=138&rft_id=info:doi/10.1098%2Frsif.2017.0844&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1742-5662&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1742-5662&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1742-5662&client=summon |