Adaptive deep neural networks methods for high-dimensional partial differential equations

DOI10.1016/j.jcp.2022.111232OpenAlexW4224222807WikidataQ114163303 ScholiaQ114163303MaRDI QIDQ2671349

Zong Zhang, Qingsong Zou, Shaojie Zeng

Publication date: 3 June 2022

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

Full work available at URL: https://doi.org/10.1016/j.jcp.2022.111232

zbMATH Keywords

adaptive samplingdeep neural networkhigh-dimensional PDEsadaptive loss functionadaptive activation function

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 (3)

A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks ⋮ Solving seepage equation using physics-informed residual network without labeled data ⋮ Variable separated physics-informed neural networks based on adaptive weighted loss functions for blood flow model

Uses Software

• DiffSharp
• Adam
• NeuroDiffEq
• DeepXDE
• DGM
• DeepONet

Cites Work

• Unnamed Item
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