{"id":3289,"date":"2022-01-11T20:39:43","date_gmt":"2022-01-12T01:39:43","guid":{"rendered":"https:\/\/people.clas.ufl.edu\/boyland\/?page_id=3289"},"modified":"2026-03-19T08:10:24","modified_gmt":"2026-03-19T12:10:24","slug":"haizhao-yang","status":"publish","type":"page","link":"https:\/\/people.clas.ufl.edu\/boyland\/haizhao-yang\/","title":{"rendered":"Haizhao Yang"},"content":{"rendered":"\r\n<section class=\"fullwidth-text-block\">\r\n\t<div class=\"container px-0 pt-5\">\r\n\t\t<div class=\"row align-items-start\">\r\n\t\t\t<div class=\"col-12\">\r\n\t\t\t\t\n<h1 class=\"wp-block-heading\">Haizhao Yang<\/h1>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"\"><span class=\"\" style=\"font-family: Arial\">Discretization-Invariant Operator Learning: Algorithms and Theory<\/span><\/span><\/h3>\n\n\n\n\n\n\n\n\n\n<p><iframe loading=\"lazy\" title=\"Haizhao Yang colloquium\" width=\"1640\" height=\"923\" src=\"https:\/\/www.youtube.com\/embed\/PGO-HjG1-0o?feature=oembed\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe><\/p>\n\n\n\n<span class=\"\"><\/span>\n\n\n\n<span class=\"\"><span class=\"\" style=\"font-family: Arial\">Abstract:\u00a0Learning operators between infinitely dimensional spaces is an important learning task arising in wide applications in machine learning, data science, mathematical modeling and simulations, etc. This talk introduces a new discretization-invariant operator learning approach based on data-driven kernels for sparsity via deep learning. Compared to existing methods, our approach achieves attractive accuracy in solving forward and inverse problems, prediction problems, and signal processing problems with zero-shot generalization, i.e., networks trained with a fixed data structure can be applied to heterogeneous data structures without expensive re-training. Under mild conditions, quantitative generalization error will be provided to understand discretization-invariant operator learning.\u00a0<\/span><\/span>\n\n\n\r\n\t\t\t<\/div>\r\n\t\t<\/div>\r\n\t<\/div>\r\n<\/section>\r\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":146,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"featured_post":"","footnotes":"","_links_to":"","_links_to_target":""},"class_list":["post-3289","page","type-page","status-publish","hentry"],"acf":[],"_links":{"self":[{"href":"https:\/\/people.clas.ufl.edu\/boyland\/wp-json\/wp\/v2\/pages\/3289","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/people.clas.ufl.edu\/boyland\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/people.clas.ufl.edu\/boyland\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/people.clas.ufl.edu\/boyland\/wp-json\/wp\/v2\/users\/146"}],"replies":[{"embeddable":true,"href":"https:\/\/people.clas.ufl.edu\/boyland\/wp-json\/wp\/v2\/comments?post=3289"}],"version-history":[{"count":3,"href":"https:\/\/people.clas.ufl.edu\/boyland\/wp-json\/wp\/v2\/pages\/3289\/revisions"}],"predecessor-version":[{"id":3839,"href":"https:\/\/people.clas.ufl.edu\/boyland\/wp-json\/wp\/v2\/pages\/3289\/revisions\/3839"}],"wp:attachment":[{"href":"https:\/\/people.clas.ufl.edu\/boyland\/wp-json\/wp\/v2\/media?parent=3289"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}