Auto-Adaptive PINNs with Applications to Phase Transitions
Fuente:
arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866908943335292928 |
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| author | Buck, Kevin Kim, Woojeong |
| author_facet | Buck, Kevin Kim, Woojeong |
| contents | We propose an adaptive sampling method for the training of Physics Informed Neural Networks (PINNs) which allows for sampling based on an arbitrary problem-specific heuristic which may depend on the network and its gradients. In particular we focus our analysis on the Allen-Cahn equations, attempting to accurately resolve the characteristic interfacial regions using a PINN without any post-hoc resampling. In experiments, we show the effectiveness of these methods over residual-adaptive frameworks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_23999 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Auto-Adaptive PINNs with Applications to Phase Transitions Buck, Kevin Kim, Woojeong Numerical Analysis Machine Learning We propose an adaptive sampling method for the training of Physics Informed Neural Networks (PINNs) which allows for sampling based on an arbitrary problem-specific heuristic which may depend on the network and its gradients. In particular we focus our analysis on the Allen-Cahn equations, attempting to accurately resolve the characteristic interfacial regions using a PINN without any post-hoc resampling. In experiments, we show the effectiveness of these methods over residual-adaptive frameworks. |
| title | Auto-Adaptive PINNs with Applications to Phase Transitions |
| topic | Numerical Analysis Machine Learning |
| url | https://arxiv.org/abs/2510.23999 |