Conservative physics-informed neural networks on discrete domains for conservation laws: applications to forward and inverse problems

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Publication:2184334

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DOI10.1016/j.cma.2020.113028zbMath1442.92002OpenAlexW3015865829MaRDI QIDQ2184334

Ameya D. Jagtap, Ehsan Kharazmi, George Em. Karniadakis

Publication date: 28 May 2020

Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.cma.2020.113028

zbMATH Keywords

conservation laws domain decomposition inverse problems machine learning cPINN mortar PINN

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Navier-Stokes equations for incompressible viscous fluids (76D05) Neural networks for/in biological studies, artificial life and related topics (92B20) Numerical methods for partial differential equations, initial value and time-dependent initial-boundary value problems (65M99) Computational methods for problems pertaining to biology (92-08)

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