Extremization to fine tune physics informed neural networks for solving boundary value problems (Q6591803)

scientific article; zbMATH DE number 7900553

Language	Label	Description	Also known as
English	Extremization to fine tune physics informed neural networks for solving boundary value problems	scientific article; zbMATH DE number 7900553

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instance of

scholarly article

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title

Extremization to fine tune physics informed neural networks for solving boundary value problems (English)

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author

Abhiram Anand Thiruthummal

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Sergiy Shelyag

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Eun-Jin Kim

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published in

Communications in Nonlinear Science and Numerical Simulation

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publication date

22 August 2024

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zbMATH Keywords

physics informed neural networks

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theory of functional connections

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boundary value problems

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PDEs

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MaRDI profile type

Publication

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cites work

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Discontinuity computing using physics-informed neural networks

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A reconstruction-based cell-centered high-order finite volume method for incompressible viscous flow simulation on unstructured meshes

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Multi-Scale Deep Neural Network (MscaleDNN) Methods for Oscillatory Stokes Flows in Complex Domains

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Physics-informed neural networks for solving forward and inverse flow problems via the Boltzmann-BGK formulation

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Mechanism of the production of small eddies from large one.

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A survey of several finite difference methods for systems of nonlinear hyperbolic conservation laws

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Physics-informed neural networks for high-speed flows

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Q4148902

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A derivative-free method for solving elliptic partial differential equations with deep neural networks

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Discontinuous neural networks and discontinuity learning

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Identifiers

Mathematics Subject Classification ID

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10.1016/J.CNSNS.2024.108129

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Sitelinks

Mathematics(1 entry)

mardi Publication:6591803