{"id":3691,"date":"2025-02-05T11:03:07","date_gmt":"2025-02-05T17:03:07","guid":{"rendered":"https:\/\/wordpress.cels.anl.gov\/lans-seminars\/?post_type=tribe_events&#038;p=3691"},"modified":"2025-02-05T11:03:38","modified_gmt":"2025-02-05T17:03:38","slug":"3691","status":"publish","type":"tribe_events","link":"https:\/\/wordpress.cels.anl.gov\/lans-seminars\/event\/3691\/","title":{"rendered":"LANS Seminar"},"content":{"rendered":"<p><strong>Seminar Title: <\/strong>Low-discrepancy designs via graph neural networks<\/p>\n<p><strong>Speaker: <\/strong>Nathan Kirk, Senior Research Associate in, The Center for Interdisciplinary Scientific Computation, Illinois Institute of Technology<\/p>\n<p><strong>Date\/Time:<\/strong> Thursday, February 6, 2025\/ 2:30 PM \u2013 3:30 PM (In-Person)<br \/>\n<strong>Location:\u00a0<\/strong><em>See Meeting URL on the cels-seminars website which will require an Argonne login.<\/em><\/p>\n<p class=\"p1\"><strong>Description: <\/strong>Message-Passing Monte Carlo (MPMC) is the first machine-learning method for the generation of low-discrepancy (space-filling) point sets. We leverage tools from Geometric Deep Learning and base our model on Graph Neural Networks. MPMC achieves state-of-the-art performance with respect to discrepancy, a uniformity measure for space-filling designs, and is superior to previous methods by a significant margin. In fact, MPMC points are empirically shown to be either optimal or near-optimal with respect to the discrepancy in every instance where the optimal discrepancy can be determined. Lastly, we show a recent extension generating representative points from an arbitrary probability distribution given the density function.<\/p>\n<p><strong>Bio: <\/strong>Nathan Kirk is a Senior Research Associate in the Center for Interdisciplinary Scientific Computation at Illinois Institute of Technology. Before that, he was a Postdoctoral Scholar at University of Waterloo, Canada and got his PhD from Queen\u2019s University Belfast, Northern Ireland. Nathan\u2019s main research interests are Monte Carlo methods and variants and implementing state-of-the-art machine learning methods in an attempt to enhance and optimize sampling procedures.<\/p>\n<p class=\"p1\"><em>Please note that the meeting URL for this event can be seen on the cels-seminars website which requires an Argonne login.<\/em><\/p>\n<div class=\"tribe-events-single-event-description tribe-events-content\">\n<p>See all upcoming talks at\u00a0<a href=\"https:\/\/www.anl.gov\/mcs\/lans-seminars\">https:\/\/www.anl.gov\/mcs\/lans-seminars<\/a><\/p>\n<\/div>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Seminar Title: Low-discrepancy designs via graph neural networks Speaker: Nathan Kirk, Senior Research Associate in, The Center for Interdisciplinary Scientific Computation, Illinois Institute of Technology Date\/Time: Thursday, February 6, 2025\/ 2:30 PM \u2013 3:30 PM (In-Person) Location:\u00a0See Meeting URL on &hellip; <a href=\"https:\/\/wordpress.cels.anl.gov\/lans-seminars\/event\/3691\/\">Continue reading <span class=\"meta-nav\">&rarr;<\/span><\/a><\/p>\n","protected":false},"author":976,"featured_media":0,"template":"","meta":{"_acf_changed":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"_tribe_events_status":"","_tribe_events_status_reason":"","footnotes":""},"tags":[],"tribe_events_cat":[2],"class_list":["post-3691","tribe_events","type-tribe_events","status-publish","hentry","tribe_events_cat-seminar","cat_seminar"],"acf":[],"_links":{"self":[{"href":"https:\/\/wordpress.cels.anl.gov\/lans-seminars\/wp-json\/wp\/v2\/tribe_events\/3691","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/wordpress.cels.anl.gov\/lans-seminars\/wp-json\/wp\/v2\/tribe_events"}],"about":[{"href":"https:\/\/wordpress.cels.anl.gov\/lans-seminars\/wp-json\/wp\/v2\/types\/tribe_events"}],"author":[{"embeddable":true,"href":"https:\/\/wordpress.cels.anl.gov\/lans-seminars\/wp-json\/wp\/v2\/users\/976"}],"version-history":[{"count":4,"href":"https:\/\/wordpress.cels.anl.gov\/lans-seminars\/wp-json\/wp\/v2\/tribe_events\/3691\/revisions"}],"predecessor-version":[{"id":3698,"href":"https:\/\/wordpress.cels.anl.gov\/lans-seminars\/wp-json\/wp\/v2\/tribe_events\/3691\/revisions\/3698"}],"wp:attachment":[{"href":"https:\/\/wordpress.cels.anl.gov\/lans-seminars\/wp-json\/wp\/v2\/media?parent=3691"}],"wp:term":[{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/wordpress.cels.anl.gov\/lans-seminars\/wp-json\/wp\/v2\/tags?post=3691"},{"taxonomy":"tribe_events_cat","embeddable":true,"href":"https:\/\/wordpress.cels.anl.gov\/lans-seminars\/wp-json\/wp\/v2\/tribe_events_cat?post=3691"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}