Deep Adaptive sampling for numerical PDEs
Event details
| Date | 04.03.2024 |
| Hour | 16:15 › 17:15 |
| Speaker | Prof. Tao Zhou (Academy of Mathematics and Systems Sciences, Chinese Academy of Sciences, Beijing) |
| Location | |
| Category | Conferences - Seminars |
| Event Language | English |
Computational Mathematics Seminar
Abstract: We present a deep adaptive sampling method for solving PDEs where deep neural networks are utilized to approximate the solutions. More precisely, we propose the failure informed adaptive sampling for PINNs and an adaptive important sampling scheme for deep Ritz. Both approaches can adaptively refine the training set with the goal of reducing the failure probability. Applications to both forward and inverse PDEs problems will be presented.
Abstract: We present a deep adaptive sampling method for solving PDEs where deep neural networks are utilized to approximate the solutions. More precisely, we propose the failure informed adaptive sampling for PINNs and an adaptive important sampling scheme for deep Ritz. Both approaches can adaptively refine the training set with the goal of reducing the failure probability. Applications to both forward and inverse PDEs problems will be presented.
Practical information
- Informed public
- Free
Organizer
- Fabio Nobile
Contact
- Fabio Nobile, Rachel Bordelais