Adaptive trajectories sampling for solving PDEs with deep learning methods-Reference-Cited by-同舟云学术

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Adaptive trajectories sampling for solving PDEs with deep learning methods

Published:2024-11 Issue: Volume:481 Page:128928
ISSN:0096-3003
Container-title:Applied Mathematics and Computation
language:en
Short-container-title:Applied Mathematics and Computation

Author:

Chen Xingyu^ORCID,Cen Jianhuan^ORCID,Zou Qingsong

Publisher

Elsevier BV

Reference46 articles.

1. ADLGM: an efficient adaptive sampling deep learning Galerkin method;Aristotelous;J. Comput. Phys.,2023

2. Bridging traditional and machine learning-based algorithms for solving PDEs: the random feature method;Chen;J. Mach. Learn.,2022

3. A method for representing periodic functions and enforcing exactly periodic boundary conditions with deep neural networks;Dong;J. Comput. Phys.,2021

4. The deep Ritz method: a deep learning-based numerical algorithm for solving variational problems;E;Commun. Math. Stat.,2018

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