{"id":788849,"date":"2021-10-26T23:15:21","date_gmt":"2021-10-27T06:15:21","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-project&#038;p=788849"},"modified":"2021-11-25T18:55:04","modified_gmt":"2021-11-26T02:55:04","slug":"learn-the-dynamics","status":"publish","type":"msr-project","link":"https:\/\/www.microsoft.com\/en-us\/research\/project\/learn-the-dynamics\/","title":{"rendered":"Learn the Dynamics"},"content":{"rendered":"<section class=\"mb-3 moray-highlight\">\n\t<div class=\"card-img-overlay mx-lg-0\">\n\t\t<div class=\"card-background bg-gray-200 has-background- card-background--full-bleed\">\n\t\t\t\t\t<\/div>\n\t\t<!-- Foreground -->\n\t\t<div class=\"card-foreground d-flex mt-md-n5 my-lg-5 px-g px-lg-0\">\n\t\t\t<!-- Container -->\n\t\t\t<div class=\"container d-flex mt-md-n5 my-lg-5 align-self-center\">\n\t\t\t\t<!-- Card wrapper -->\n\t\t\t\t<div class=\"w-100 w-lg-col-5\">\n\t\t\t\t\t<!-- Card -->\n\t\t\t\t\t<div class=\"card material-md-card py-5 px-md-5\">\n\t\t\t\t\t\t<div class=\"card-body \">\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\n<h1 id=\"learn-the-dynamics\">Learn the Dynamics<\/h1>\n\n\n\n<p><\/p>\n\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t<\/div>\n\t\t<\/div>\n\t<\/div>\n<\/section>\n\n\n\n\n\n<p>The laws of nature are described as dynamical systems. Learning dynamics, as a new AI technique, faces the following challenges: (1) the existing pure deep learning models are not that reliable in the scientific field, i.e., they can fit the data but whether it can generalize is still unknown; (2) its training is slow and unstable due to the intrinsic slowly solvable and unstable properties of large-scale dynamical systems; (3) with the different final goals, the expectation for the dynamic learning is different.&nbsp; We develop new deep learning-based methods for physical dynamics identification by incorporating physical priors as conservation law, group symmetry, and symbolic elements.&nbsp;&nbsp; We also focus on new requirements when learning the dynamic given the goal is to control the system.<\/p>\n\n\n\n\n\n<ul class=\"has-medium-font-size wp-block-list\"><li>Ziming Liu, Bohan Wang, Qi Meng, Wei Chen, Max Tegmark, Tie-Yan Liu, Machine-Learning Non-Conservative Dynamics for New-Physics Detection, <em>Physical Review E<\/em>, 2021.<\/li><li>Shiqi Gong, Qi Meng, Yue Wang, Lijun Wu, Wei Chen, Zhi-Ming Ma, Tie-Yan Liu, Incorporating NODE with Pre-trained Neural Differential Operator for Learning Dynamics, <em>arXiv preprint arXiv: 2106.04166<\/em>, 2021.<\/li><li>Weitao Du, He Zhang, Yuanqi Du, Qi Meng, Wei Chen, Bin Shao, Tie-Yan Liu, Equivariant vector field network for many-body system modeling, <em>arXiv preprint arXiv: 2110.14811<\/em>, 2021.<\/li><\/ul>\n\n\n","protected":false},"excerpt":{"rendered":"<p>The laws of nature are described as dynamical systems. Learning dynamics, as a new AI technique, faces the following challenges: (1) the existing pure deep learning models are not that reliable in the scientific field, i.e., they can fit the data but whether it can generalize is still unknown; (2) its training is slow and [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"footnotes":""},"research-area":[13556],"msr-locale":[268875],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-788849","msr-project","type-msr-project","status-publish","hentry","msr-research-area-artificial-intelligence","msr-locale-en_us","msr-archive-status-active"],"msr_project_start":"","related-publications":[],"related-downloads":[],"related-videos":[],"related-groups":[],"related-events":[],"related-opportunities":[],"related-posts":[],"related-articles":[],"tab-content":[],"slides":[],"related-researchers":[{"type":"user_nicename","display_name":"Yue Wang","user_id":39931,"people_section":"Section name 0","alias":"yuwang5"}],"msr_research_lab":[199560],"msr_impact_theme":[],"_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/788849"}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/msr-project"}],"version-history":[{"count":9,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/788849\/revisions"}],"predecessor-version":[{"id":797590,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/788849\/revisions\/797590"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=788849"}],"wp:term":[{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=788849"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=788849"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=788849"},{"taxonomy":"msr-pillar","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-pillar?post=788849"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}