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About
President
Governance
Partner Institutions
Visit
People
Management
Faculty
Postdocs
Visiting Scholars
Administration
Academic Support
Research
Research Groups
Courses
Seminars
Join Us
Faculty
Postdocs
Students
Events
Conferences
Workshops
Forum
Life @ BIMSA
Accommodation
Transportation
Facilities
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News
News
Announcement
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Qiuzhen College, Tsinghua University
Yau Mathematical Sciences Center, Tsinghua University (YMSC)
Tsinghua Sanya International  Mathematics Forum (TSIMF)
Shanghai Institute for Mathematics and  Interdisciplinary Sciences (SIMIS)
BIMSA > BIMSA Digital Economy Lab Seminar Multi-scale Modeling and Machine Learning for Discovering Hidden Laws in Complex Systems
Multi-scale Modeling and Machine Learning for Discovering Hidden Laws in Complex Systems
Organizers
Liyan Han , Zhen Li , Fei Long , Ke Tang , Yu Wang
Speaker
Wuyue Yang
Time
Friday, March 28, 2025 3:00 PM - 4:00 PM
Venue
A3-2a-302
Online
Zoom 637 734 0280 (BIMSA)
Abstract
Deriving hidden laws from observational data and predicting the future state of systems is both fundamental and challenging. In fields such as chemical reactions, physical processes, and biological systems, these problems often involve highly complex and nonlinear dynamic behaviors. Multi-scale modeling can simultaneously consider system behaviors at different time and spatial scales, capturing interactions between different levels. Combined with machine learning algorithms, it can automatically identify patterns at different scales within a multi-scale framework, thus more efficiently and accurately revealing system dynamics when facing complex, large amounts of data. We apply machine learning methods to multi-scale modeling of chemical reactions, illustrating how multi-scale modeling can significantly reduce the computational cost of machine learning, and how machine learning algorithms can automatically perform model simplification in systems with time scale separation. We also developed a two-phase approach for learning interaction kernels in stochastic many-particle systems. Numerical experiments demonstrate excellent performance across various cases, including cubic, repulsion-attraction power-law, double-well potentials.
Speaker 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."
Beijing Institute of Mathematical Sciences and Applications
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