Development and optimization ofphysics-informed neural networks for solving partial differential equations

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dc.contributor.authorBatyr Sharimbayev
dc.contributor.authorShirali Kadyrov
dc.contributor.authorAleksei Kavokin
dc.date.accessioned2025-10-13T10:28:08Z
dc.date.available2025-10-13T10:28:08Z
dc.date.issued2025
dc.description.abstractThis study investigates the application of physics-informed neural networks (PINNs) for solving Poissonequations in both 1D and 2D domains and compares them with finite difference method. Additionally, thestudy explores the capability of multi-task learning with PINNs, where the network not only predicts thesolution but also estimates unknown parameters. In the case of a second-order differential equation witha varying coefficient, PINNs successfully approximated both the source term and the varying coefficientwhile achieving low training loss. The model demonstrated excellent generalization capabilities and accuratereconstruction of the underlying system parameters, showing the potential of PINNs in complex physicalsimulations.
dc.identifier.citationBatyr Sharimbayev, Shirali Kadyrov, Aleksei Kavokin / Development and optimization ofphysics-informed neural networks forsolving partial differential equations / Journal of Emerging Technologies and Computing (JETC), Vol. 1 No. 1 / SDU University / 2025
dc.identifier.urihttps://repository.sdu.edu.kz/handle/123456789/2047
dc.language.isoen
dc.publisherSDU University
dc.rightsAttribution-NonCommercial-ShareAlike 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/
dc.subjectnumerical analysis
dc.subjectmulti-task learning
dc.subjectdeep learning
dc.subjectPINNs
dc.subjectFDM
dc.titleDevelopment and optimization ofphysics-informed neural networks for solving partial differential equations
dc.typeArticle

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