Fourier warm start for physics-informed neural networks
Physics-informed neural networks (PINNs) have shown applicability in a wide range of engineering domains. However, there remain some challenges in their use, namely, PINNs are notoriously difficult to train and prone to failure when dealing with complex tasks with multi-frequency patterns or steep g...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/180175 |