Physics Informed Neural Networks for heat conduction with phase change
Fuente:
arXiv
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909354107600896 |
|---|---|
| author | Madir, Bahae-Eddine Luddens, Francky Lothodé, Corentin Danaila, Ionut |
| author_facet | Madir, Bahae-Eddine Luddens, Francky Lothodé, Corentin Danaila, Ionut |
| contents | We study numerical algorithms to solve a specific Partial Differential Equation (PDE), namely the Stefan problem, using Physics Informed Neural Networks (PINNs). This problem describes the heat propagation in a liquid-solid phase change system. It implies a heat equation and a discontinuity at the interface where the phase change occurs. In the context of PINNs, this model leads to difficulties in the learning process, especially near the interface of phase change. We present different strategies that can be used in this context. We illustrate our results and compare with classical solvers for PDEs (finite differences). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_14216 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Physics Informed Neural Networks for heat conduction with phase change Madir, Bahae-Eddine Luddens, Francky Lothodé, Corentin Danaila, Ionut Numerical Analysis We study numerical algorithms to solve a specific Partial Differential Equation (PDE), namely the Stefan problem, using Physics Informed Neural Networks (PINNs). This problem describes the heat propagation in a liquid-solid phase change system. It implies a heat equation and a discontinuity at the interface where the phase change occurs. In the context of PINNs, this model leads to difficulties in the learning process, especially near the interface of phase change. We present different strategies that can be used in this context. We illustrate our results and compare with classical solvers for PDEs (finite differences). |
| title | Physics Informed Neural Networks for heat conduction with phase change |
| topic | Numerical Analysis |
| url | https://arxiv.org/abs/2410.14216 |