Solving Euler equations with Multiple Discontinuities via Separation-Transfer Physics-Informed Neural Networks
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866909624062443520 |
|---|---|
| author | Wang, Chuanxing Luo, Hui Wang, Kai Zhu, Guohuai Luo, Mingxing |
| author_facet | Wang, Chuanxing Luo, Hui Wang, Kai Zhu, Guohuai Luo, Mingxing |
| contents | Despite the remarkable progress of physics-informed neural networks (PINNs) in scientific computing, they continue to face challenges when solving hydrodynamic problems with multiple discontinuities. In this work, we propose Separation-Transfer Physics Informed Neural Networks (ST-PINNs) to address such problems. By sequentially resolving discontinuities from strong to weak and leveraging transfer learning during training, ST-PINNs significantly reduce the problem complexity and enhance solution accuracy. To the best of our knowledge, this is the first study to apply a PINNs-based approach to the two-dimensional unsteady planar shock refraction problem, offering new insights into the application of PINNs to complex shock-interface interactions. Numerical experiments demonstrate that ST-PINNs more accurately capture sharp discontinuities and substantially reduce solution errors in hydrodynamic problems involving multiple discontinuities. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_20361 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Solving Euler equations with Multiple Discontinuities via Separation-Transfer Physics-Informed Neural Networks Wang, Chuanxing Luo, Hui Wang, Kai Zhu, Guohuai Luo, Mingxing Fluid Dynamics Machine Learning Despite the remarkable progress of physics-informed neural networks (PINNs) in scientific computing, they continue to face challenges when solving hydrodynamic problems with multiple discontinuities. In this work, we propose Separation-Transfer Physics Informed Neural Networks (ST-PINNs) to address such problems. By sequentially resolving discontinuities from strong to weak and leveraging transfer learning during training, ST-PINNs significantly reduce the problem complexity and enhance solution accuracy. To the best of our knowledge, this is the first study to apply a PINNs-based approach to the two-dimensional unsteady planar shock refraction problem, offering new insights into the application of PINNs to complex shock-interface interactions. Numerical experiments demonstrate that ST-PINNs more accurately capture sharp discontinuities and substantially reduce solution errors in hydrodynamic problems involving multiple discontinuities. |
| title | Solving Euler equations with Multiple Discontinuities via Separation-Transfer Physics-Informed Neural Networks |
| topic | Fluid Dynamics Machine Learning |
| url | https://arxiv.org/abs/2505.20361 |