报告题目：Automated discovery of fundamental variables hidden in experimental data

主讲人：黄旷

单位：香港中文大学

时间：9月27日8:30

地点： 学院南研教室

摘要：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.

简介：黄旷，2022年博士毕业于美国哥伦比亚大学应用数学专业，师从杜强教授，现为香港中文大学数学系研究助理教授。研究方向：非局部模型在交通流建模中的应用与数值计算、基于深度学习和流形学习的复杂系统建模、平均场博弈。