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Adaptive task decomposition physics-informed neural networks

Published:2024-01 Issue: Volume:418 Page:116561
ISSN:0045-7825
Container-title:Computer Methods in Applied Mechanics and Engineering
language:en
Short-container-title:Computer Methods in Applied Mechanics and Engineering

Author:

Yang Jianchuan,Liu Xuanqi,Diao Yu,Chen Xi,Hu Haikuo

Publisher

Elsevier BV

Subject

Computer Science Applications,General Physics and Astronomy,Mechanical Engineering,Mechanics of Materials,Computational Mechanics

Reference46 articles.

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

2. Universal approximation using feedforward neural networks: a survey of some existing methods, and some new results;Scarselli;Neural Netw.,1998

3. A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics;Haghighat;Comput. Methods Appl. Mech. Eng.,2021

4. Physics-informed neural networks (PINNs) for fluid mechanics: a review;Cai;Acta Mech. Sin.,2021

5. Physics-informed neural networks for high-speed flows;Mao;Comput. Methods Appl. Mech. Eng.,2020

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Deep neural network method to predict the dynamical system response under random excitation of combined Gaussian and Poisson white noises;Chaos, Solitons & Fractals;2024-08

2. Anti-derivatives approximator for enhancing physics-informed neural networks;Computer Methods in Applied Mechanics and Engineering;2024-06

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