Physics-guided deep learning methods and applications
Deep learning (DL)-based methods have been widely applied across various science and engineering disciplines. This course focuses on deep learning techniques within the context of AI for Science, with a specific emphasis on their applications in applied geophysics. The curriculum starts with some broadly applicable methodologies drawn from the latest literature, which are general and not limited to specific fields, and then introduces some of their improvements, extensions, and applications. In addition, the lecturer will discuss some specific issues and the answers provided by AI tools and also introduce some related ideas and research work. All attendees are welcome to ask questions and communicate with each other.
讲师
日期
2026年10月13日 至 12月29日
位置
| Weekday | Time | Venue | Online | ID | Password |
|---|---|---|---|---|---|
| 周二 | 13:30 - 16:55 | Shuimo | ZOOM 09 | 230 432 7880 | BIMSA |
修课要求
Basic knowledge on deep learning methods, geophysics, and the Python language.
课程大纲
1. Introduction to neural network (NN) architectures, physics-guided deep learning, current mainstream topics
Basic methodology:
2. Physics-informed neural networks (PINNs)
3. Operator learning (a): DeepONet, FNO, and Transformer
4. Operator learning (b): Design NN’s input, auxiliary sample approach
Latest updates and important applications:
5. Selection of training samples in operator learning
6. Some applications related to operator learning
7. Equation discovery and field reconstruction
8. Neural network surrogate modeling method
AI for Geophysics:
9. NN-based inversion methods and auxiliary sample approach
10. Seismic rock physics modeling and parameter inversion
11. Application in full wave inversion (FWI) and ambient noise tomography (ANT)
12. Some research results, communication and interaction
Basic methodology:
2. Physics-informed neural networks (PINNs)
3. Operator learning (a): DeepONet, FNO, and Transformer
4. Operator learning (b): Design NN’s input, auxiliary sample approach
Latest updates and important applications:
5. Selection of training samples in operator learning
6. Some applications related to operator learning
7. Equation discovery and field reconstruction
8. Neural network surrogate modeling method
AI for Geophysics:
9. NN-based inversion methods and auxiliary sample approach
10. Seismic rock physics modeling and parameter inversion
11. Application in full wave inversion (FWI) and ambient noise tomography (ANT)
12. Some research results, communication and interaction
参考资料
1. The latest published articles related to the topic of AI for Science.
2. Some notes summarized from articles, and the discussion results provided by AI tools.
3. The ideas, practical applications, and research results of the lecturer.
2. Some notes summarized from articles, and the discussion results provided by AI tools.
3. The ideas, practical applications, and research results of the lecturer.
听众
Graduate
, 博士后
, Researcher
视频公开
不公开
笔记公开
不公开
语言
中文
讲师介绍
熊繁升，现任北京雁栖湖应用数学研究院助理研究员，曾任北京应用物理与计算数学研究所所聘博士后。先后毕业于中国地质大学(北京)、清华大学，美国耶鲁大学联合培养博士。研究兴趣主要集中于基于机器学习算法(DNN、PINN、DeepONet等)求解微分方程模型正/反问题及其在地球物理波传播问题中的应用，相关成果发表在JGR Solid Earth、GJI、Geophysics等期刊上。