Nonparametric inference of interaction laws in systems of agents from trajectory data [Applied Mathematics]

Inferring the laws of interaction in agent-based systems from observational data is a fundamental challenge in a wide variety of disciplines. We propose a nonparametric statistical learning approach for distance-based interactions, with no reference or assumption on their analytical form, given data consisting of sampled trajectories of interacting agents. We...

Source: Proceedings of the National Academy of Sciences - July 15, 2019 Category: Science Authors: Fei Lu, Ming Zhong, Sui Tang, Mauro Maggioni Tags: PNAS Plus Source Type: research