Importance of Nyquist-Shannon Sampling in Training of Physics-Informed Neural Networks-Reference-Cited by-同舟云学术

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Importance of Nyquist-Shannon Sampling in Training of Physics-Informed Neural Networks

Published:2024-06-30 Issue: Volume: Page:1-8
ISSN:
Container-title:2024 International Joint Conference on Neural Networks (IJCNN)
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Author:

Ooi Chin Chun¹,Huang Anran²,Wei Zhao¹,Qin Shiyao³,Wong Jian Cheng⁴,Chiu Pao-Hsiung⁴,Dao My Ha⁴

Affiliation:

1. Centre for Frontier AI Research,Agency for Science, Technology and Research (A*STAR),Singapore,Republic of Singapore,138632

2. Anglo-Chinese Junior College,Singapore,Republic of Singapore,139745

3. Institute of High Performance Computing (IHPC),Agency for Science, Technology and Research (A*STAR),Dept of Fluid Dynamics,Singapore,Republic of Singapore,138632

4. National Junior College,Singapore,Republic of Singapore,288913

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10649807/10649898/10650694.pdf?arnumber=10650694

Reference22 articles.

1. Physics-informed machine learning

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

3. DeepXDE: A Deep Learning Library for Solving Differential Equations

4. Scientific Machine Learning Through Physics–Informed Neural Networks: Where we are and What’s Next

5. Applications of Physics-Informed Neural Networks in Power Systems - A Review

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