Dynamics-Driven Data Analysis and Complex Systems Modeling
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
Date
30th September ~ 23rd December, 2025
Location
| Weekday | Time | Venue | Online | ID | Password |
|---|---|---|---|---|---|
| Tuesday | 08:50 - 12:15 | A3-1-101 | ZOOM 06 | 537 192 5549 | BIMSA |
Syllabus
Week 1: Foundations of Dynamics and Data Science
Week 2: Nonlinear Dynamics and Chaos Theory
Week 3: Stochastic Processes and Noise-Driven Systems
Week 4: Statistical Physics and Complex Systems Principles
Week 5: Network Dynamics and Topological Structure Analysis
Week 6: Dynamical Reconstruction Methods for Time Series
Week 7: Machine Learning Applications in Dynamical Systems
Week 8: Reservoir Computing and Chaotic Dynamics
Week 9: Data-Driven System Identification and Control
Week 10: Dynamical Modeling of Biological Systems
Week 11: Complex Dynamics in Socio-Economic Systems
Week 12: Frontier Applications and Comprehensive Practice
Week 2: Nonlinear Dynamics and Chaos Theory
Week 3: Stochastic Processes and Noise-Driven Systems
Week 4: Statistical Physics and Complex Systems Principles
Week 5: Network Dynamics and Topological Structure Analysis
Week 6: Dynamical Reconstruction Methods for Time Series
Week 7: Machine Learning Applications in Dynamical Systems
Week 8: Reservoir Computing and Chaotic Dynamics
Week 9: Data-Driven System Identification and Control
Week 10: Dynamical Modeling of Biological Systems
Week 11: Complex Dynamics in Socio-Economic Systems
Week 12: Frontier Applications and Comprehensive Practice
Audience
Undergraduate
, Advanced Undergraduate
, Graduate
, Postdoc
, Researcher
Video Public
No
Notes Public
No
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."