Some AI methods and their applications
Artificial Intelligence (AI) methods have been widely applied in various fields. This course primarily introduces AI methods related to deep learning and large language model (LLM), as well as their applications in geophysics. We first introduce some approaches based on the latest papers, and then introduce their applications in geophysics, including background knowledge, methods and numerical examples. In addition, during this semester's course, the lecturer will also discuss the solutions to some scientific issues provided by LLM tools. There will be interactive time in each class, and all attendees are welcome to ask questions and communicate with each other.
讲师
日期
2026年03月31日 至 06月23日
位置
| Weekday | Time | Venue | Online | ID | Password |
|---|---|---|---|---|---|
| 周二 | 13:30 - 16:55 | A3-1-301 | ZOOM 09 | 230 432 7880 | BIMSA |
修课要求
Basic knowledge on deep learning methods, seismic rock physics, and the Python language.
课程大纲
1. Introduction to neural network (NN)-based approaches, discussion on current mainstream topics
The Latest Updates on Some Approaches:
2. Physics-informed neural networks (PINNs)
3. Physics-informed Kolmogorov-Arnold networks (PIKANs)
4. Neural operators (NOs)
Transformer-based LLMs:
5. Introduction to Transformer Models
6. Some complex neural network architectures
7. Application in partial differential equations (PDEs) discovery
8. Application in solving parameterized PDEs
The Application in Geophysics Problems:
9. AI-based inversion methods and applications in parameter inversion
10. Establishment of dynamic equations for seismic rock physics
11. Application of AI methods in computational geophysics
12. Some review literatures, Communication, and Interaction
The Latest Updates on Some Approaches:
2. Physics-informed neural networks (PINNs)
3. Physics-informed Kolmogorov-Arnold networks (PIKANs)
4. Neural operators (NOs)
Transformer-based LLMs:
5. Introduction to Transformer Models
6. Some complex neural network architectures
7. Application in partial differential equations (PDEs) discovery
8. Application in solving parameterized PDEs
The Application in Geophysics Problems:
9. AI-based inversion methods and applications in parameter inversion
10. Establishment of dynamic equations for seismic rock physics
11. Application of AI methods in computational geophysics
12. Some review literatures, Communication, and Interaction
参考资料
1. The latest published papers related to deep learning methods, as well as some important papers from previous courses.
2. Textbooks related to the Transformer model.
3. The discussion results provided by LLM-based tools, and some notes summarized from papers.
2. Textbooks related to the Transformer model.
3. The discussion results provided by LLM-based tools, and some notes summarized from papers.
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。