{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/nonparametric-inference-of-interaction-laws","title":"Nonparametric inference of interaction laws in systems of agents from trajectory data","arxiv_id":"1812.06003","date":"2018-12-14","proceeding":null,"authors":["Fei Lu","Mauro Maggioni","Sui Tang","Ming Zhong"],"abstract":"Inferring the laws of interaction between particles and agents in complex\ndynamical systems from observational data is a fundamental challenge in a wide\nvariety of disciplines. We propose a non-parametric statistical learning\napproach to estimate the governing laws of distance-based interactions, with no\nreference or assumption about their analytical form, from data consisting\ntrajectories of interacting agents. We demonstrate the effectiveness of our\nlearning approach both by providing theoretical guarantees, and by testing the\napproach on a variety of prototypical systems in various disciplines. These\nsystems include homogeneous and heterogeneous agents systems, ranging from\nparticle systems in fundamental physics to agent-based systems modeling opinion\ndynamics under the social influence, prey-predator dynamics, flocking and\nswarming, and phototaxis in cell dynamics.","url_abs":"http://arxiv.org/abs/1812.06003v4","url_pdf":"http://arxiv.org/pdf/1812.06003v4.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"nonparametric-inference-of-interaction-laws","repo_url":"https://github.com/MingZhongCodes/LearningDynamics","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1812.06003","atlas_url":"https://app.syntology.ai/?focus=1812.06003","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}