{"id":575,"date":"2019-11-09T01:23:26","date_gmt":"2019-11-09T01:23:26","guid":{"rendered":"http:\/\/pages.charlotte.edu\/colloquium\/?p=575"},"modified":"2019-11-09T01:26:45","modified_gmt":"2019-11-09T01:26:45","slug":"monday-november-18-1100am-1200pm-at-the-conference-room","status":"publish","type":"post","link":"https:\/\/pages.charlotte.edu\/colloquium\/blog\/2019\/11\/09\/monday-november-18-1100am-1200pm-at-the-conference-room\/","title":{"rendered":"Monday, November 18, 11:00am-12:00pm at the conference room"},"content":{"rendered":"\n<p><strong>Speaker<\/strong>: Dr. Lu Lu from Brown University (hosted by Xingjie Li).<\/p>\n\n\n\n<p><strong>Title<\/strong>: Learning dynamical systems and differential equations with deep learning: physics-informed and data-driven<br><strong>Abstract<\/strong>: Deep learning has achieved remarkable success in diverse applications; however, its use in learning dynamical systems and partial differential equations (PDEs) has emerged only recently. These learning approaches can be either physics-informed or data-driven. In the physics-informed approach, I have improved the physics-informed neural networks (PINNs) and developed the library DeepXDE for solving different types of PDEs, including integro-differential equations, fractional PDEs, and stochastic PDEs. In the data-driven approach, I have developed the deep operator network (DeepONet) based on the universal approximation theorem of operators to learn dynamical systems accurately and efficiently from a relatively small dataset. In addition, I will present my work on the deep learning theory of optimization and generalization, and the application of applying multi-fidelity neural networks to predict mechanical properties of solid materials.<br><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Speaker: Dr. Lu Lu from Brown University (hosted by Xingjie Li). Title: Learning dynamical systems and differential equations with deep learning: physics-informed and data-drivenAbstract: Deep learning has achieved remarkable success in diverse applications; however, its use in learning dynamical systems and partial differential equations (PDEs) has emerged only recently. These learning approaches can be either [&hellip;]<\/p>\n","protected":false},"author":1211,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[1],"tags":[],"class_list":["post-575","post","type-post","status-publish","format-standard","hentry","category-spring-2022"],"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_shortlink":"https:\/\/wp.me\/p3kCtT-9h","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/pages.charlotte.edu\/colloquium\/wp-json\/wp\/v2\/posts\/575","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/pages.charlotte.edu\/colloquium\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/pages.charlotte.edu\/colloquium\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/pages.charlotte.edu\/colloquium\/wp-json\/wp\/v2\/users\/1211"}],"replies":[{"embeddable":true,"href":"https:\/\/pages.charlotte.edu\/colloquium\/wp-json\/wp\/v2\/comments?post=575"}],"version-history":[{"count":3,"href":"https:\/\/pages.charlotte.edu\/colloquium\/wp-json\/wp\/v2\/posts\/575\/revisions"}],"predecessor-version":[{"id":578,"href":"https:\/\/pages.charlotte.edu\/colloquium\/wp-json\/wp\/v2\/posts\/575\/revisions\/578"}],"wp:attachment":[{"href":"https:\/\/pages.charlotte.edu\/colloquium\/wp-json\/wp\/v2\/media?parent=575"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/pages.charlotte.edu\/colloquium\/wp-json\/wp\/v2\/categories?post=575"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/pages.charlotte.edu\/colloquium\/wp-json\/wp\/v2\/tags?post=575"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}