题  目：Automated discovery of fundamental variables hidden in experimental data

内容简介：Physical laws can be described as relationships between state variables that give a complete and non-redundant description of the relevant dynamical systems. Most data-driven methods for modeling physical phenomena assume that observed data streams already correspond to given state variables. However, despite the prevalence of computing power and AI, the process of identifying a set of state variables themselves from experiment data has resisted automation. We propose a framework for determining how many state variables an observed system is likely to have, and what these variables might be, directly from video streams. We also demonstrate the effectiveness of this approach using video recordings of a variety of dynamical systems, ranging from elastic double pendulum to fire flames.

报告人：Kuang Huang

报告人简介：Dr. Kuang Huang is a Research Assistant Professor in the Department of Mathematics at The Chinese University of Hong Kong. He completed his undergraduate studies at the School of Mathematics and Statistics at Wuhan University in 2017 and obtained his Ph.D. from the Department of Applied Physics and Applied Mathematics at Columbia University in 2022. His research interests include nonlocal models and their applications in traffic flow modeling, mean field games, and physics-informed and data-driven modeling of dynamical systems.

时  间：2024年1月28日（周日）下午14：00开始

地  点：腾讯会议：63129598867

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