A practical PINN framework for multi-scale problems with multi-magnitude loss terms-Reference-Cited by-同舟云学术

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A practical PINN framework for multi-scale problems with multi-magnitude loss terms

Published:2024-08 Issue: Volume:510 Page:113112
ISSN:0021-9991
Container-title:Journal of Computational Physics
language:en
Short-container-title:Journal of Computational Physics

Author:

Wang Yong,Yao Yanzhong,Guo Jiawei,Gao Zhiming

Funder

National Key Defense Laboratory of Computational Physics

National Key Research and Development Program of China

National Natural Science Foundation of China

Publisher

Elsevier BV

Reference31 articles.

1. On the eigenvector bias of Fourier feature networks: from regression to solving multi-scale pdes with physics-informed neural networks;Wang;Comput. Methods Appl. Mech. Eng.,2021

2. Artificial neural networks for solving ordinary and partial differential equations;Lagaris;IEEE Trans. Neural Netw.,1998

3. Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations;Raissi;J. Comput. Phys.,2019

4. Physics-informed machine learning;Karniadakis;Nat. Rev. Phys.,2021

5. DeepXDE: a deep learning library for solving differential equations;Lu;SIAM Rev.,2021

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1. Online multi-scenario impedance spectra generation for batteries based on small-sample learning;Cell Reports Physical Science;2024-08

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