opPINN: physics-informed neural network with operator learning to approximate solutions to the Fokker-Planck-Landau equation

DOI10.1016/j.jcp.2023.112031OpenAlexW4321766500MaRDI QIDQ2689626

Juhi Jang, Jaeyong Lee, Hyung-Ju Hwang

Publication date: 13 March 2023

Published in: Journal of Computational Physics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2207.01765

zbMATH Keywords

Fokker-Planck-Landau equationkinetic theory of gasesdeep learningoperator learningphysics-informed neural network (PINN)

Mathematics Subject Classification ID

Numerical methods for partial differential equations, initial value and time-dependent initial-boundary value problems (65Mxx) Partial differential equations of mathematical physics and other areas of application (35Qxx) Time-dependent statistical mechanics (dynamic and nonequilibrium) (82Cxx)

Uses Software

• DGM
• DeepONet

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• Unnamed Item
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