Variable linear transformation improved physics-informed neural networks to solve thin-layer flow problems
Author:
Funder
National Natural Science Foundation of China
Key Technologies Research and Development Program
National Key Research and Development Program of China
Publisher
Elsevier BV
Reference29 articles.
1. Scientific machine learning through physics-informed neural networks: where we are and what's next;Cuomo;J. Sci. Comput.,2022
2. Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations;Raissi;J. Comput. Phys.,2019
3. DeepXDE: a deep learning library for solving differential equations;Lu;SIAM Rev.,2021
4. Self-adaptive loss balanced Physics-informed neural networks;Xiang;Neurocomputing,2022
5. Inverse Dirichlet weighting enables reliable training of physics informed neural networks;Maddu;Machine Learning-Science and Technology,2022
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