Learning time-dependent PDEs with a linear and nonlinear separate convolutional neural network

DOI10.1016/j.jcp.2021.110928OpenAlexW4206672419WikidataQ114163379 ScholiaQ114163379MaRDI QIDQ2135244

Wei Hua Cai, Yijun Zhao, Jiagang Qu

Publication date: 4 May 2022

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

Full work available at URL: https://doi.org/10.1016/j.jcp.2021.110928

zbMATH Keywords

partial differential equationsdiscrete dynamic systemdata-driven methodconvolutional neural networkPDEs discovering

Mathematics Subject Classification ID

Artificial intelligence (68Txx) Numerical methods for partial differential equations, initial value and time-dependent initial-boundary value problems (65Mxx) Numerical methods for partial differential equations, boundary value problems (65Nxx)

Related Items (1)

A framework based on symbolic regression coupled with eXtended physics-informed neural networks for gray-box learning of equations of motion from data

Uses Software

• PyTorch
• Adam
• DeepXDE
• PDE-Net
• DGM
• ConvPDE-UQ
• PhyCNN
• PhyGeoNet
• XPINNs

Cites Work

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