Differentiable Hash Encoding for Physics-Informed Neural Networks
Author:
Affiliation:
1. Beijing Institute of Technology (BIT),School of Aerospace Engineering (SAE),Beijing,China
2. Nanyang Technological University (NTU),School of Computer Science and Engineering (SCSE),Singapore
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx8/10605128/10605229/10605240.pdf?arnumber=10605240
Reference12 articles.
1. Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
2. Scientific Machine Learning Through Physics–Informed Neural Networks: Where we are and What’s Next
3. Characterizing possible failure modes in physics-informed neural networks;Krishnapriyan;Advances in Neural Information Processing Systems,2021
4. LSA-PINN: Linear Boundary Connectivity Loss for Solving PDEs on Complex Geometry
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