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.

Lecturer

Wuyue Yang

Date

2nd September, 2025 ~ -

Location

Weekday	Time	Venue	Online	ID	Password
Tuesday	08:50 - 12:15		-	-	-

Audience

Undergraduate , Advanced Undergraduate , Graduate , Postdoc , Researcher

Video Public

Notes Public

Language

Chinese , English

Lecturer Intro

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."