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

([西财新闻] 发布于 :2019-01-09 )

光华讲坛——社会名流与企业家论坛第 5204 期

 

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

主講人:Dr. Ming Zhong, Johns Hopkins University 

主持人:經濟數學學院 王琪教授

時間2019年1月11日(星期五)下午16:00-17:00

地點:西南財經大學柳林校區通博樓B412會議室

主辦單位:經濟數學學院 科研

 

主講人簡介:

Ming Zhong(鍾明)博士于2016年获得美国马里兰大學數學博士学位,现在约翰霍普金斯大學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。

主要內容:

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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