Intelligent computing for magnetohydrodynamic micropolar nanofluid with stratification using Levenberg–Marquardt backpropagation algorithm
The magnetohydrodynamic (MHD) micropolar nanofluid with stratification is evaluated in this work by integrated numerical computing using the Levenberg Marquardt backpropagation (LMBB) optimization technique, an artificial neural network (ANN) approach. After that, model is condensed to a set of prob...
Saved in:
| Published in: | Journal of computational science Vol. 92; p. 102727 |
|---|---|
| Main Authors: | , , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Elsevier B.V
01.12.2025
|
| Subjects: | |
| ISSN: | 1877-7503 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | The magnetohydrodynamic (MHD) micropolar nanofluid with stratification is evaluated in this work by integrated numerical computing using the Levenberg Marquardt backpropagation (LMBB) optimization technique, an artificial neural network (ANN) approach. After that, model is condensed to a set of problems with boundary values, which are resolved utilizing the proposed method LMBB algorithm and a numerical technique BVP4c. The LMBB approach is an iterative approach for figuring out the least of a function that is not linear, is distinct as the addition of squares. The outcomes are also cross-checked against those of earlier studies and the MATLAB’s BVP4c solver for validation. The mapping of velocity, concentration and temperature profiles from the input to results is another use of neural networking. These results show the accuracy level of the predictions and improvements made by ANN. To generalize a dataset, the BVP4c techniques’ performance is utilized to lower error of mean square. Data based on the ratio of training (80 %), validation (10 %) and testing (10 %) is used by the ANN-based LMBB backpropagation optimization technique. Histograms and function fitness are utilized to verify the algorithm’s dependability. For fluid dynamics, numerical methods and ANN perform incredibly well together, and this could result in new developments across a wide range of fields. The results of this study may aid in the optimization of fluid systems, leading to higher productivity and efficiency in a range of engineering applications. |
|---|---|
| AbstractList | The magnetohydrodynamic (MHD) micropolar nanofluid with stratification is evaluated in this work by integrated numerical computing using the Levenberg Marquardt backpropagation (LMBB) optimization technique, an artificial neural network (ANN) approach. After that, model is condensed to a set of problems with boundary values, which are resolved utilizing the proposed method LMBB algorithm and a numerical technique BVP4c. The LMBB approach is an iterative approach for figuring out the least of a function that is not linear, is distinct as the addition of squares. The outcomes are also cross-checked against those of earlier studies and the MATLAB’s BVP4c solver for validation. The mapping of velocity, concentration and temperature profiles from the input to results is another use of neural networking. These results show the accuracy level of the predictions and improvements made by ANN. To generalize a dataset, the BVP4c techniques’ performance is utilized to lower error of mean square. Data based on the ratio of training (80 %), validation (10 %) and testing (10 %) is used by the ANN-based LMBB backpropagation optimization technique. Histograms and function fitness are utilized to verify the algorithm’s dependability. For fluid dynamics, numerical methods and ANN perform incredibly well together, and this could result in new developments across a wide range of fields. The results of this study may aid in the optimization of fluid systems, leading to higher productivity and efficiency in a range of engineering applications. |
| ArticleNumber | 102727 |
| Author | Ullah, Ikram Hassan, Waqar Ul Zhang, Hong-Na Li, Feng-Chen Shukat, Saira Haq, Ikram Ul Li, Xiao-Bin |
| Author_xml | – sequence: 1 givenname: Ikram Ul surname: Haq fullname: Haq, Ikram Ul organization: State Key Laboratory of Engines, School of Mechanical Engineering, Tianjin University, Tianjin 300350, China – sequence: 2 givenname: Saira surname: Shukat fullname: Shukat, Saira organization: Department of Mathematics, University of Sialkot, Sialkot 51040, Pakistan – sequence: 3 givenname: Ikram surname: Ullah fullname: Ullah, Ikram organization: Department of Natural Sciences and Humanities, University of Engineering and Technology, Mardan 23200, Pakistan – sequence: 4 givenname: Waqar Ul surname: Hassan fullname: Hassan, Waqar Ul organization: State Key Laboratory of Engines, School of Mechanical Engineering, Tianjin University, Tianjin 300350, China – sequence: 5 givenname: Hong-Na surname: Zhang fullname: Zhang, Hong-Na organization: State Key Laboratory of Engines, School of Mechanical Engineering, Tianjin University, Tianjin 300350, China – sequence: 6 givenname: Xiao-Bin surname: Li fullname: Li, Xiao-Bin organization: State Key Laboratory of Engines, School of Mechanical Engineering, Tianjin University, Tianjin 300350, China – sequence: 7 givenname: Feng-Chen orcidid: 0000-0003-3271-7949 surname: Li fullname: Li, Feng-Chen email: lifch@tju.edu.cn organization: State Key Laboratory of Engines, School of Mechanical Engineering, Tianjin University, Tianjin 300350, China |
| BookMark | eNp9kE1OwzAQhb0oEqX0Aqx8gZbYaetYYoMqfioVsYG15diT1CGxi-0WdccB2HFDToKjsGakmZFG8z7NvAs0ss4CQlckm5OMrK6beeNUmNOMLtOAMspGaEwKxmZsmeXnaBpCk6XIi4KTfIy-NjZC25oabMTKdftDNLbGlfO4k7WF6HYn7Z0-WdkZhVN6t3et9NhK66r2YDT-MHGHQ_QymsqoVJ3Fh9BjtnAEW4Kvfz6_n6R_P0ivIy6letsnjKyHXdnWzidGd4nOKtkGmP71CXq9v3tZP862zw-b9e12pugyjzO20BRoqSnLOZegOfAFEA4VKVlR0RWtJC9XC54RKIjOMyhLlTorpKY58CKfIDpw0zMheKjE3ptO-pMgmehtFI3obRS9jWKwMYluBhGky44GvAjKgFWgjQcVhXbmP_kvddaGaA |
| Cites_doi | 10.1108/HFF-02-2021-0103 10.1063/1.4935649 10.1002/adfm.201906041 10.1007/s10973-018-7482-6 10.1038/s41598-022-06458-3 10.1155/2022/9888379 10.3390/fluids7030116 10.1142/S0217979223502375 10.1080/10407782.2023.2212861 10.1007/s00162-020-00542-y 10.1016/0020-7225(64)90005-9 10.1007/s10409-021-01154-3 10.1038/s41598-021-97458-2 10.1186/1556-276X-6-300 10.1080/17458080.2017.1285445 10.1007/s10973-019-08434-y 10.3390/en15010316 10.1016/j.aej.2024.10.003 10.3390/cryst11111315 10.1016/j.csite.2024.104378 10.1016/0022-247X(72)90106-0 10.1016/j.petrol.2018.04.019 |
| ContentType | Journal Article |
| Copyright | 2025 Elsevier B.V. |
| Copyright_xml | – notice: 2025 Elsevier B.V. |
| DBID | AAYXX CITATION |
| DOI | 10.1016/j.jocs.2025.102727 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Sciences (General) Business |
| ExternalDocumentID | 10_1016_j_jocs_2025_102727 S1877750325002042 |
| GroupedDBID | --K --M .~1 0R~ 1B1 1~. 1~5 4.4 457 4G. 5VS 7-5 71M 8P~ 9DU AAEDT AAEDW AAIKJ AAKOC AALRI AAOAW AAQFI AATTM AAXKI AAXUO AAYFN AAYWO ABBOA ABFRF ABJNI ABMAC ABWVN ABXDB ACDAQ ACGFO ACGFS ACLOT ACNNM ACRLP ACRPL ACVFH ACZNC ADBBV ADCNI ADEZE ADMUD ADNMO AEBSH AEFWE AEIPS AEKER AENEX AEUPX AFJKZ AFPUW AFTJW AGHFR AGUBO AGYEJ AHZHX AIALX AIEXJ AIGII AIIUN AIKHN AITUG AKBMS AKRWK AKYEP ALMA_UNASSIGNED_HOLDINGS AMRAJ ANKPU AOUOD APXCP AXJTR BKOJK BLXMC EBS EFJIC EFKBS EFLBG EJD EP3 FDB FEDTE FIRID FNPLU FYGXN GBLVA GBOLZ HVGLF HZ~ J1W KOM M41 MO0 N9A O-L O9- OAUVE P-8 P-9 P2P PC. Q38 ROL SDF SES SPC SPCBC SSV SSZ T5K UNMZH ~G- ~HD AAYXX CITATION |
| ID | FETCH-LOGICAL-c253t-74d2e2bd27399aed9e94e19ef1b78f262fa9b64901e81d30ebbc1d378ad23e983 |
| ISICitedReferencesCount | 0 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001591847900001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1877-7503 |
| IngestDate | Sat Nov 29 07:21:14 EST 2025 Wed Dec 10 14:34:08 EST 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Levenberg-Marquardt Riga plate Nanofluid Double stratification, Suction/Injection Machine learning MHD Artificial neural network |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c253t-74d2e2bd27399aed9e94e19ef1b78f262fa9b64901e81d30ebbc1d378ad23e983 |
| ORCID | 0000-0003-3271-7949 |
| ParticipantIDs | crossref_primary_10_1016_j_jocs_2025_102727 elsevier_sciencedirect_doi_10_1016_j_jocs_2025_102727 |
| PublicationCentury | 2000 |
| PublicationDate | December 2025 2025-12-00 |
| PublicationDateYYYYMMDD | 2025-12-01 |
| PublicationDate_xml | – month: 12 year: 2025 text: December 2025 |
| PublicationDecade | 2020 |
| PublicationTitle | Journal of computational science |
| PublicationYear | 2025 |
| Publisher | Elsevier B.V |
| Publisher_xml | – name: Elsevier B.V |
| References | Zhang, Noack (bib26) 2021; 37 Kouki, Shukat, Ullah, Alam, Ali (bib12) 2025; 111 Geng, Al-Rashed, Mahmoudi, Alsagri, Shahsavar, Talebizadehsardari (bib4) 2020; 139 Sofos, Stavrogiannis, Exarchou-Kouveli, Akabua, Charilas, Karakasidis (bib24) 2022; 7 Shoaib, Nisar, Raja, Saad, Tabassum, Rafiq, Ullah (bib27) 2022 Eringen (bib15) 1966 Brunton, Hemati, Taira (bib23) 2020; 34 Pattnaik, Bhatti, Mishra, Abbas, Bég (bib17) 2022; 2022 Agwu, Akpabio, Alabi, Dosunmu (bib25) 2018; 167 Jakeer, Rupa, Reddy, Rashad (bib28) 2023; 12 Ullah, Waqas, Hayat, Alsaedi, Ijaz Khan (bib9) 2019; 135 Eringen (bib14) 1964; 2 Fedele, Colla, Bobbo, Barison, Agresti (bib6) 2011; 6 Ullah, Shukat, Albakri, Khan, Galal, Jamshed (bib11) 2023; 37 Uddin, Ullah, Raja, Shoaib, Islam, Zobaer, Alshahrani (bib29) 2021; 11 Zhang, Nazar, Bhatti, Michaelides (bib3) 2022; 32 Choudhary, Khurana, Kumar, Subudhi (bib5) 2017; 12 Rafique, Alotaibi, Ibrar, Khan (bib19) 2022; 15 Hayat, Rashid, Imtiaz, Alsaedi (bib7) 2015; 5 Mohapatra, Panda, Mishra (bib8) 2024 Eringen (bib16) 1972; 38 Khan, Zuhra, Islam, Raja, Ali (bib10) 2023; 138 Akbar, Zamir, Muhammad (bib30) 2024; 50 Ali, Ali, Asogwa, Apsari (bib21) 2024; 85 Amin, Ullah, Shukat, Kouki, Ahmad, Alam, Khan (bib13) 2024; 58 Wang, Zhang, Snoussi, Zhang (bib22) 2020; 30 Bilal, Saeed, Gul, Kumam, Mukhtar, Kumam (bib18) 2022; 12 Choi (bib1) 1998 Manna (bib2) 2009; 89 Alotaibi, Rafique (bib20) 2021; 11 Jakeer (10.1016/j.jocs.2025.102727_bib28) 2023; 12 Sofos (10.1016/j.jocs.2025.102727_bib24) 2022; 7 Choi (10.1016/j.jocs.2025.102727_bib1) 1998 Choudhary (10.1016/j.jocs.2025.102727_bib5) 2017; 12 Ali (10.1016/j.jocs.2025.102727_bib21) 2024; 85 Eringen (10.1016/j.jocs.2025.102727_bib14) 1964; 2 Amin (10.1016/j.jocs.2025.102727_bib13) 2024; 58 Khan (10.1016/j.jocs.2025.102727_bib10) 2023; 138 Kouki (10.1016/j.jocs.2025.102727_bib12) 2025; 111 Zhang (10.1016/j.jocs.2025.102727_bib3) 2022; 32 Wang (10.1016/j.jocs.2025.102727_bib22) 2020; 30 Pattnaik (10.1016/j.jocs.2025.102727_bib17) 2022; 2022 Eringen (10.1016/j.jocs.2025.102727_bib15) 1966 Hayat (10.1016/j.jocs.2025.102727_bib7) 2015; 5 Alotaibi (10.1016/j.jocs.2025.102727_bib20) 2021; 11 Agwu (10.1016/j.jocs.2025.102727_bib25) 2018; 167 Akbar (10.1016/j.jocs.2025.102727_bib30) 2024; 50 Rafique (10.1016/j.jocs.2025.102727_bib19) 2022; 15 Mohapatra (10.1016/j.jocs.2025.102727_bib8) 2024 Ullah (10.1016/j.jocs.2025.102727_bib11) 2023; 37 Brunton (10.1016/j.jocs.2025.102727_bib23) 2020; 34 Shoaib (10.1016/j.jocs.2025.102727_bib27) 2022 Geng (10.1016/j.jocs.2025.102727_bib4) 2020; 139 Fedele (10.1016/j.jocs.2025.102727_bib6) 2011; 6 Manna (10.1016/j.jocs.2025.102727_bib2) 2009; 89 Bilal (10.1016/j.jocs.2025.102727_bib18) 2022; 12 Zhang (10.1016/j.jocs.2025.102727_bib26) 2021; 37 Ullah (10.1016/j.jocs.2025.102727_bib9) 2019; 135 Eringen (10.1016/j.jocs.2025.102727_bib16) 1972; 38 Uddin (10.1016/j.jocs.2025.102727_bib29) 2021; 11 |
| References_xml | – start-page: 1 year: 2024 end-page: 15 ident: bib8 article-title: Exploring heat transfer enhancement: machine learning predictions using artificial neural network for Water-Based cu and CuO micropolar nanofluid transportation over a radiating surface publication-title: BioNanoScience – volume: 89 start-page: 21 year: 2009 end-page: 33 ident: bib2 article-title: Synthesis, characterization and application of nanofluid—an overview publication-title: J. Indian Inst. Sci. – volume: 2 start-page: 205 year: 1964 end-page: 217 ident: bib14 article-title: Simple microfluids publication-title: Int. J. Eng. Sci. – volume: 32 start-page: 740 year: 2022 end-page: 760 ident: bib3 article-title: Stability analysis on the kerosene nanofluid flow with hybrid zinc/aluminum-oxide (ZnO-Al2O3) nanoparticles under lorentz force publication-title: Int. J. Numer. Methods Heat. Fluid Flow. – volume: 6 start-page: 300 year: 2011 ident: bib6 article-title: Experimental stability analysis of different water-based nanofluids publication-title: Nanoscale Res. Lett. – start-page: 1 year: 1966 end-page: 18 ident: bib15 article-title: Theory of micropolar fluids publication-title: J. Math. Mech. – volume: 85 start-page: 1889 year: 2024 end-page: 1902 ident: bib21 article-title: Transient rotating three-dimensional flow of micropolar fluid induced by Riga plate: finite element approach publication-title: Numer. Heat. Transf. Part A Appl. – volume: 30 start-page: 1906041 year: 2020 ident: bib22 article-title: Machine learning approaches for thermoelectric materials research publication-title: Adv. Funct. Mater. – volume: 15 start-page: 316 year: 2022 ident: bib19 article-title: Stratified flow of micropolar nanofluid over Riga plate: numerical analysis publication-title: Energies – volume: 167 start-page: 300 year: 2018 end-page: 315 ident: bib25 article-title: Artificial intelligence techniques and their applications in drilling fluid engineering: a review publication-title: J. Pet. Sci. Eng. – volume: 34 start-page: 333 year: 2020 end-page: 337 ident: bib23 article-title: Special issue on machine learning and data-driven methods in fluid dynamics publication-title: Theor. Comput. Fluid Dyn. – volume: 2022 start-page: 9888379 year: 2022 ident: bib17 article-title: Mixed convective-radiative dissipative magnetized micropolar nanofluid flow over a stretching surface in porous media with double stratification and chemical reaction effects: ADM-Padé computation publication-title: J. Math. – volume: 12 start-page: 410 year: 2023 end-page: 427 ident: bib28 article-title: Artificial neural network model of non-Darcy MHD sutterby hybrid nanofluid flow over a curved permeable surface: solar energy applications. publication-title: Research – volume: 37 start-page: 2350237 year: 2023 ident: bib11 article-title: Thermal performance of aqueous alumina–titania hybrid nanomaterials dispersed in rotating channel publication-title: Int. J. Mod. Phys. B – volume: 38 start-page: 480 year: 1972 end-page: 496 ident: bib16 article-title: Theory of thermomicrofluids publication-title: J. Math. Anal. Appl. – volume: 7 start-page: 116 year: 2022 ident: bib24 article-title: Current trends in fluid research in the era of artificial intelligence: a review publication-title: Fluids – year: 1998 ident: bib1 article-title: Nanofluid technology: current status and future research (No. ANL/ET/CP-97466) – volume: 139 start-page: 1553 year: 2020 end-page: 1564 ident: bib4 article-title: Characterization of the nanoparticles, the stability analysis and the evaluation of a new hybrid nano-oil thermal conductivity publication-title: J. Therm. Anal. Calorim. – volume: 111 start-page: 160 year: 2025 end-page: 170 ident: bib12 article-title: Keller-box based computational investigation of magnetized gravity-driven micropolar nanofluid flow past an exponentially contracting surface with cross diffusion effect and engineering applications publication-title: Alex. Eng. J. – volume: 11 start-page: 1315 year: 2021 ident: bib20 article-title: Numerical analysis of micro-rotation effect on nanofluid flow for vertical Riga plate publication-title: Crystals – volume: 138 start-page: 107 year: 2023 ident: bib10 article-title: Modeling and simulation of Maxwell nanofluid flows in the presence of lorentz and Darcy–Forchheimer forces: toward a new approach on Buongiorno’s model using artificial neural network (ANN) publication-title: Eur. Phys. J. – volume: 12 start-page: 140 year: 2017 end-page: 151 ident: bib5 article-title: Stability analysis of Al2O3/water nanofluids publication-title: J. Exp. Nanosci. – volume: 135 start-page: 1021 year: 2019 end-page: 1030 ident: bib9 article-title: Thermally radiated squeezed flow of magneto-nanofluid between two parallel disks with chemical reaction publication-title: J. Therm. Anal. Calorim. – volume: 58 year: 2024 ident: bib13 article-title: Lorentz force and solar energy case study on CNTs and pollytetrafluoroethylene (PTFE) paraffin oil-based hybrid nanofluid flow through a porous divergent/convergent channel publication-title: Case Stud. Therm. Eng. – volume: 50 year: 2024 ident: bib30 article-title: Levenberg-Marquardt technique analysis of thermal and concentration storage in cone-disk apparatus with neural network-enhancement publication-title: Therm. Sci. Eng. Prog. – start-page: 1 year: 2022 end-page: 29 ident: bib27 article-title: Intelligent networks knacks for numerical treatment of three-dimensional Darcy–Forchheimer williamson nanofluid model past a stretching surface publication-title: Waves Random Complex Media – volume: 11 start-page: 19239 year: 2021 ident: bib29 article-title: The intelligent networks for double-diffusion and MHD analysis of thin film flow over a stretched surface publication-title: Sci. Rep. – volume: 5 year: 2015 ident: bib7 article-title: Magnetohydrodynamic (MHD) stretched flow of nanofluid with power-law velocity and chemical reaction publication-title: AIP Adv. – volume: 12 start-page: 2542 year: 2022 ident: bib18 article-title: Parametric simulation of micropolar fluid with thermal radiation across a porous stretching surface publication-title: Sci. Rep. – volume: 37 start-page: 1715 year: 2021 end-page: 1717 ident: bib26 article-title: Artificial intelligence in fluid mechanics publication-title: Acta Mech. Sin. – volume: 32 start-page: 740 issue: 2 year: 2022 ident: 10.1016/j.jocs.2025.102727_bib3 article-title: Stability analysis on the kerosene nanofluid flow with hybrid zinc/aluminum-oxide (ZnO-Al2O3) nanoparticles under lorentz force publication-title: Int. J. Numer. Methods Heat. Fluid Flow. doi: 10.1108/HFF-02-2021-0103 – volume: 5 issue: 11 year: 2015 ident: 10.1016/j.jocs.2025.102727_bib7 article-title: Magnetohydrodynamic (MHD) stretched flow of nanofluid with power-law velocity and chemical reaction publication-title: AIP Adv. doi: 10.1063/1.4935649 – volume: 30 start-page: 1906041 issue: 5 year: 2020 ident: 10.1016/j.jocs.2025.102727_bib22 article-title: Machine learning approaches for thermoelectric materials research publication-title: Adv. Funct. Mater. doi: 10.1002/adfm.201906041 – volume: 135 start-page: 1021 year: 2019 ident: 10.1016/j.jocs.2025.102727_bib9 article-title: Thermally radiated squeezed flow of magneto-nanofluid between two parallel disks with chemical reaction publication-title: J. Therm. Anal. Calorim. doi: 10.1007/s10973-018-7482-6 – volume: 12 start-page: 2542 issue: 1 year: 2022 ident: 10.1016/j.jocs.2025.102727_bib18 article-title: Parametric simulation of micropolar fluid with thermal radiation across a porous stretching surface publication-title: Sci. Rep. doi: 10.1038/s41598-022-06458-3 – volume: 2022 start-page: 9888379 issue: 1 year: 2022 ident: 10.1016/j.jocs.2025.102727_bib17 article-title: Mixed convective-radiative dissipative magnetized micropolar nanofluid flow over a stretching surface in porous media with double stratification and chemical reaction effects: ADM-Padé computation publication-title: J. Math. doi: 10.1155/2022/9888379 – volume: 7 start-page: 116 issue: 3 year: 2022 ident: 10.1016/j.jocs.2025.102727_bib24 article-title: Current trends in fluid research in the era of artificial intelligence: a review publication-title: Fluids doi: 10.3390/fluids7030116 – volume: 37 start-page: 2350237 issue: 24 year: 2023 ident: 10.1016/j.jocs.2025.102727_bib11 article-title: Thermal performance of aqueous alumina–titania hybrid nanomaterials dispersed in rotating channel publication-title: Int. J. Mod. Phys. B doi: 10.1142/S0217979223502375 – volume: 85 start-page: 1889 issue: 11 year: 2024 ident: 10.1016/j.jocs.2025.102727_bib21 article-title: Transient rotating three-dimensional flow of micropolar fluid induced by Riga plate: finite element approach publication-title: Numer. Heat. Transf. Part A Appl. doi: 10.1080/10407782.2023.2212861 – volume: 34 start-page: 333 issue: 4 year: 2020 ident: 10.1016/j.jocs.2025.102727_bib23 article-title: Special issue on machine learning and data-driven methods in fluid dynamics publication-title: Theor. Comput. Fluid Dyn. doi: 10.1007/s00162-020-00542-y – volume: 2 start-page: 205 issue: 2 year: 1964 ident: 10.1016/j.jocs.2025.102727_bib14 article-title: Simple microfluids publication-title: Int. J. Eng. Sci. doi: 10.1016/0020-7225(64)90005-9 – volume: 37 start-page: 1715 issue: 12 year: 2021 ident: 10.1016/j.jocs.2025.102727_bib26 article-title: Artificial intelligence in fluid mechanics publication-title: Acta Mech. Sin. doi: 10.1007/s10409-021-01154-3 – volume: 12 start-page: 410 issue: 3 year: 2023 ident: 10.1016/j.jocs.2025.102727_bib28 article-title: Artificial neural network model of non-Darcy MHD sutterby hybrid nanofluid flow over a curved permeable surface: solar energy applications. propulsion and power publication-title: Research – volume: 138 start-page: 107 issue: 1 year: 2023 ident: 10.1016/j.jocs.2025.102727_bib10 article-title: Modeling and simulation of Maxwell nanofluid flows in the presence of lorentz and Darcy–Forchheimer forces: toward a new approach on Buongiorno’s model using artificial neural network (ANN) publication-title: Eur. Phys. J. – start-page: 1 year: 2022 ident: 10.1016/j.jocs.2025.102727_bib27 article-title: Intelligent networks knacks for numerical treatment of three-dimensional Darcy–Forchheimer williamson nanofluid model past a stretching surface publication-title: Waves Random Complex Media – volume: 11 start-page: 19239 issue: 1 year: 2021 ident: 10.1016/j.jocs.2025.102727_bib29 article-title: The intelligent networks for double-diffusion and MHD analysis of thin film flow over a stretched surface publication-title: Sci. Rep. doi: 10.1038/s41598-021-97458-2 – volume: 50 year: 2024 ident: 10.1016/j.jocs.2025.102727_bib30 article-title: Levenberg-Marquardt technique analysis of thermal and concentration storage in cone-disk apparatus with neural network-enhancement publication-title: Therm. Sci. Eng. Prog. – volume: 6 start-page: 300 year: 2011 ident: 10.1016/j.jocs.2025.102727_bib6 article-title: Experimental stability analysis of different water-based nanofluids publication-title: Nanoscale Res. Lett. doi: 10.1186/1556-276X-6-300 – volume: 12 start-page: 140 issue: 1 year: 2017 ident: 10.1016/j.jocs.2025.102727_bib5 article-title: Stability analysis of Al2O3/water nanofluids publication-title: J. Exp. Nanosci. doi: 10.1080/17458080.2017.1285445 – volume: 139 start-page: 1553 year: 2020 ident: 10.1016/j.jocs.2025.102727_bib4 article-title: Characterization of the nanoparticles, the stability analysis and the evaluation of a new hybrid nano-oil thermal conductivity publication-title: J. Therm. Anal. Calorim. doi: 10.1007/s10973-019-08434-y – volume: 15 start-page: 316 issue: 1 year: 2022 ident: 10.1016/j.jocs.2025.102727_bib19 article-title: Stratified flow of micropolar nanofluid over Riga plate: numerical analysis publication-title: Energies doi: 10.3390/en15010316 – start-page: 1 year: 1966 ident: 10.1016/j.jocs.2025.102727_bib15 article-title: Theory of micropolar fluids publication-title: J. Math. Mech. – volume: 111 start-page: 160 year: 2025 ident: 10.1016/j.jocs.2025.102727_bib12 article-title: Keller-box based computational investigation of magnetized gravity-driven micropolar nanofluid flow past an exponentially contracting surface with cross diffusion effect and engineering applications publication-title: Alex. Eng. J. doi: 10.1016/j.aej.2024.10.003 – volume: 11 start-page: 1315 issue: 11 year: 2021 ident: 10.1016/j.jocs.2025.102727_bib20 article-title: Numerical analysis of micro-rotation effect on nanofluid flow for vertical Riga plate publication-title: Crystals doi: 10.3390/cryst11111315 – volume: 58 year: 2024 ident: 10.1016/j.jocs.2025.102727_bib13 article-title: Lorentz force and solar energy case study on CNTs and pollytetrafluoroethylene (PTFE) paraffin oil-based hybrid nanofluid flow through a porous divergent/convergent channel publication-title: Case Stud. Therm. Eng. doi: 10.1016/j.csite.2024.104378 – volume: 38 start-page: 480 issue: 2 year: 1972 ident: 10.1016/j.jocs.2025.102727_bib16 article-title: Theory of thermomicrofluids publication-title: J. Math. Anal. Appl. doi: 10.1016/0022-247X(72)90106-0 – volume: 89 start-page: 21 issue: 1 year: 2009 ident: 10.1016/j.jocs.2025.102727_bib2 article-title: Synthesis, characterization and application of nanofluid—an overview publication-title: J. Indian Inst. Sci. – start-page: 1 year: 2024 ident: 10.1016/j.jocs.2025.102727_bib8 article-title: Exploring heat transfer enhancement: machine learning predictions using artificial neural network for Water-Based cu and CuO micropolar nanofluid transportation over a radiating surface publication-title: BioNanoScience – year: 1998 ident: 10.1016/j.jocs.2025.102727_bib1 – volume: 167 start-page: 300 year: 2018 ident: 10.1016/j.jocs.2025.102727_bib25 article-title: Artificial intelligence techniques and their applications in drilling fluid engineering: a review publication-title: J. Pet. Sci. Eng. doi: 10.1016/j.petrol.2018.04.019 |
| SSID | ssj0000388913 |
| Score | 2.3570824 |
| Snippet | The magnetohydrodynamic (MHD) micropolar nanofluid with stratification is evaluated in this work by integrated numerical computing using the Levenberg... |
| SourceID | crossref elsevier |
| SourceType | Index Database Publisher |
| StartPage | 102727 |
| SubjectTerms | Artificial neural network Double stratification, Suction/Injection Levenberg-Marquardt Machine learning MHD Nanofluid Riga plate |
| Title | Intelligent computing for magnetohydrodynamic micropolar nanofluid with stratification using Levenberg–Marquardt backpropagation algorithm |
| URI | https://dx.doi.org/10.1016/j.jocs.2025.102727 |
| Volume | 92 |
| WOSCitedRecordID | wos001591847900001&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 issn: 1877-7503 databaseCode: AIEXJ dateStart: 20100501 customDbUrl: isFulltext: true dateEnd: 99991231 titleUrlDefault: https://www.sciencedirect.com omitProxy: false ssIdentifier: ssj0000388913 providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV3NbtQwELaWFiEuiBYQ5U8-cABFqbrOj-NjhYooggqJrthb5CR29zfZzSZVufEA3HgY3ocnYRw7znZpET1wyUZWMvHufJkZz36eQegloVJwP4ncMKKZ66csdFkmmRtwtSSi_aRPZdNsgp6cRMMh-9Tr_Wz3wpzPaJ5HFxds8V9VDWOgbLV19gbqtkJhAM5B6XAEtcPxnxR_bItsVg1hvK5asuScn-WiKkZfM7CauhO9M1eEvIVa3zo5zws5q8eGj64L6kqT03PqJqnwQRV8aihhhiThfeTlUsGschKeTsEeg4XSd_DZWVGCpPk1AbCeXJuMNK64s4jLxnxNSz53BpYG8nlUT3mlU9nj0rqUAYB5ZK_vZKxWOr_7hS_hCxoxJsdBgjW-iDbLEaWu-sd13W7rHnrG8EKcRHWRgT98gk5PTPYnRarqs5Ngv7v4cgHuDcdo6YotE24SKxmxkhFrGbfQNqEBA4-wfXh8NHxv03uqyA5rmnPbuZstW5pduDmZq8OitVDn9D66Z1SEDzW2dlBP5LvoTrtFYhftGIewwq9M1fLXD9D3NdxhizsMuMNX4A53uMMWd1jhDl_GHW5why3ufn37YRGHNxCHLeIeosHbo9M371zT6sNNSeBVLvUzIkiSQTDNGBcZE8wXfSZkP6GRJCGRnCWhD8GrgAWWdyCSJIVPGvGMeIJF3iO0lRe5eIxwRvtS-IzKhEc-xGWcsRRGwJGyAx5SuYec9peOF7qiS3y9gvdQ0CojNi-CjjVjgNdf7ntyo6c8RXc72D9DW1VZi-fodnpejVflC4Ot3wvet8s |
| 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=Intelligent+computing+for+magnetohydrodynamic+micropolar+nanofluid+with+stratification+using+Levenberg%E2%80%93Marquardt+backpropagation+algorithm&rft.jtitle=Journal+of+computational+science&rft.au=Haq%2C+Ikram+Ul&rft.au=Shukat%2C+Saira&rft.au=Ullah%2C+Ikram&rft.au=Hassan%2C+Waqar+Ul&rft.date=2025-12-01&rft.issn=1877-7503&rft.volume=92&rft.spage=102727&rft_id=info:doi/10.1016%2Fj.jocs.2025.102727&rft.externalDBID=n%2Fa&rft.externalDocID=10_1016_j_jocs_2025_102727 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1877-7503&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1877-7503&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1877-7503&client=summon |