Effective Training of PINNs by Combining CMA-ES with Gradient Descent-Reference-Cited by-同舟云学术

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Effective Training of PINNs by Combining CMA-ES with Gradient Descent

Published:2024-06-30 Issue: Volume: Page:1-8
ISSN:
Container-title:2024 IEEE Congress on Evolutionary Computation (CEC)
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Author:

Liu Lin¹,Yuan Yuan²

Affiliation:

1. School of Computer Science and Engineering, Beihang University,Beijing,China,100191

2. School of Computer Science and Engineering, Beihang University,State Key Laboratory of Software Development Environment, Zhongguancun Laboratory,Beijing,China,100191

Funder

National Natural Science Foundation of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10609966/10611750/10611964.pdf?arnumber=10611964

Reference13 articles.

1. Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

2. Neuroevolution Surpasses Stochastic Gradient Descent for Physics-Informed Neural Networks;Yong;arXiv preprint,2022

3. Learning in Sinusoidal Spaces with Physics-Informed Neural Networks

4. MOPINNs

5. The CMA Evolution Strategy: A Comparing Review

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