{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T06:59:37Z","timestamp":1776063577230,"version":"3.50.1"},"reference-count":56,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2026,4,12]],"date-time":"2026-04-12T00:00:00Z","timestamp":1775952000000},"content-version":"vor","delay-in-days":101,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/100009392","name":"Prince Sattam bin Abdulaziz University","doi-asserted-by":"publisher","award":["PSAU\/2025\/R\/1447"],"award-info":[{"award-number":["PSAU\/2025\/R\/1447"]}],"id":[{"id":"10.13039\/100009392","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Computational and Mathematical Methods"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:p>\n                    Industrial applications involving coupled mass and heat transfer have motivated the present study of steady, two\u2010dimensional magnetohydrodynamic (MHD) nanofluid flow on a linearly stretching sheet, with viscous dissipation, temperature\u2010dependent viscosity, chemical reactions, and Soret effects. Traditional numerical solvers remain computationally intensive for extensive parametric analyses of these highly nonlinear multiphysics systems, particularly when fully coupled impacts are considered over stretching surfaces. This study addresses the gap by developing a novel hybrid machine learning framework, a feed\u2010forward artificial neural network (FFANN) with 10 hidden neurons and log\u2010sigmoid activation, optimized through the Levenberg\u2013Marquardt backpropagation algorithm (FFANN\u2010BLMA), trained on high\u2010fidelity reference data. The governing partial differential equations are reduced to nonlinear ordinary differential equations via similarity transformation and solved accurately using the fourth\u2010order Runge\u2013Kutta method in Mathematica. The trained model demonstrates exceptional predictive performance, with absolute errors ranging from 10\n                    <jats:sup>\u22126<\/jats:sup>\n                    to 10\n                    <jats:sup>\u22129<\/jats:sup>\n                    across the dimensionless velocity\n                    <jats:italic>f<\/jats:italic>\n                    <jats:sup>\u2032<\/jats:sup>\n                    , temperature\n                    <jats:italic>g<\/jats:italic>\n                    , and concentration\n                    <jats:italic>h<\/jats:italic>\n                    profiles for nanoparticle volume fractions\n                    <jats:italic>\u03d1<\/jats:italic>\n                    = 0.1, 0.2, and 0.3. Mean squared errors reach the order of 10\n                    <jats:sup>\u221212<\/jats:sup>\n                    to 10\n                    <jats:sup>\u221214<\/jats:sup>\n                    , and regression indices approach unity (\n                    <jats:italic>R<\/jats:italic>\n                    \u2248 1), confirming the robustness and accuracy of the FFANN\u2010BLMA framework. Parametric experiments indicate that the amplification of\n                    <jats:italic>\u03d1<\/jats:italic>\n                    suppresses the velocity profile while thickening the thermal and solutal boundary layers due to augmented effective thermal conductivity in addition to species diffusivity. The suggested FFANN\u2010BLMA paradigm offers a computationally efficient, precise, and scalable alternative to the traditional numerical solvers that allows rapid design optimization and exploration of parameters in industrial thermal systems, such as electronics cooling, stretch\u2010coating operations, and energy\u2010efficient heat exchangers. This article highlights the transformative nature of machine learning in the development of modeling capabilities in applications of complex fluid dynamics and heat transfer.\n                  <\/jats:p>","DOI":"10.1155\/cmm4\/2681643","type":"journal-article","created":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T06:29:56Z","timestamp":1776061796000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Neurocomputing Modeling of Hydromagnetic Nanofluid Flow With Chemical Reactions on Fluctuating 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