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美國約翰霍普金斯大學鐘明博士學術報告

發布:2019-01-03 13:24?????? 作者:admin??????來源:未知

報告題目:Nonparametric inference of interaction laws in systems of agents from trajectory data

人:鐘明 美國約翰霍普金斯大學

報告時間:110日下午1630

報告地點:清水河主樓A1-513

人:徐立偉 教授

 

報告摘要:

  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.

 

 

報告人簡介:

  鐘明,2016年在美國馬里蘭大學數學系獲得博士學位。2016年至今在美國約翰霍普金斯大學應用數學與統計系做博士后。主要研究領域是機器學習,逆問題和壓縮感知大地彩票,Large Eddy Simulation, Uncertainty Quantification, Numerical Solverfor Optimal Transport Plan/Wasserstein Distance等。


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