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Dr. Ming Zhong, Johns Hopkins University: Nonparametric inference of interaction laws in systems of agents from trajectory data

([西财资讯] 发布于 :2019-01-09 )

光华讲坛——社会名流与企业家论坛第 5204 期

 

主题:Nonparametric inference of interaction laws in systems of agents from trajectory data

主讲人:Dr. Ming Zhong, Johns Hopkins University 

主持人:经济数学学院 王琪教授

时间2019年1月11日(星期五)下午16:00-17:00

地点:西南财经大学柳林校区通博楼B412会议室

主办单位:经济数学学院 科研

 

主讲人概况:

Ming Zhong(钟明)博士于2016年获得美国马里兰大学数学博士学位,现在约翰霍普金斯大学从事博士后研究工作;其主要研究领域为自组织动力学、压缩感应数据恢复、偏微分方程数值计算等。

主要内容:

Inferring the laws of interaction between particles and agents in complex dynamical systems from observational data is a fundamental challenge in a wide variety of disciplines. We propose a non-parametric statistical learning approach to estimate the governing laws of distance-based interactions, with no reference or assumption about their analytical form, from data consisting trajectories of interacting agents. We demonstrate the effectiveness of our learning approach both by providing theoretical guarantees, and by testing the approach on a variety of prototypical systems in various disciplines. These systems include homogeneous and heterogeneous agents systems, ranging from particle systems in fundamental physics to agent-based systems modeling opinion dynamics under the social influence, prey-predator dynamics, flocking and swarming, and phototaxis in cell dynamics.


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