AI-Driven Modeling and Simulation
This course explores cutting-edge techniques where artificial intelligence enhances the modeling and simulation of complex dynamical systems. Traditional approaches often struggle with high dimensionality, nonlinearity, and sparse data, but AI methods—such as data-driven system identification, AI-augmented agent-based modeling, and surrogate modeling—offer powerful solutions. Students will learn how machine learning can extract governing equations from data (e.g., SINDy, Neural ODEs), optimize agent behaviors in simulations, and construct efficient reduced-order models for computationally expensive systems. Applications span physics, biology, economics, and engineering, emphasizing real-world case studies and hands-on implementation.
讲师
日期
2025年09月02日 至 -
位置
| Weekday | Time | Venue | Online | ID | Password |
|---|---|---|---|---|---|
| 周二 | 08:50 - 12:15 | - | - | - |
听众
Undergraduate
, Advanced Undergraduate
, Graduate
, 博士后
, Researcher
视频公开
不公开
笔记公开
不公开
语言
中文
, 英文
讲师介绍
Dr. Wuyue Yang is currently an Assistant Professor at BIMSA. She received her PhD degree from Tsinghua University in 2022 and was honored as an Outstanding Graduate of Beijing. Her main research directions are artificial intelligence, machine learning theory and applications. She has published papers in internationally renowned academic journals such as "Journal of Computational Physics," "Journal of Chemical Physics," and "Physics of Fluids," with over 1,000 Google citations. She is the PI of a National Natural Science Foundation of China Youth Fund project and has participated as a researcher in National Key R&D Program Special Projects. She serves as a review expert for international journals including "BMC Infectious Diseases" and "AIMS Mathematics." She teaches courses including "Theory and Application of Sparse Identification of Nonlinear Dynamics (SINDy)," "Complex System Dynamics and Control," and "Collective Dynamics of Active Matter."