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UID:3691-1738852200-1738855800@wordpress.cels.anl.gov
SUMMARY:LANS Seminar
DESCRIPTION:Seminar Title: Low-discrepancy designs via graph neural networks \nSpeaker: Nathan Kirk\, Senior Research Associate in\, The Center for Interdisciplinary Scientific Computation\, Illinois Institute of Technology \nDate/Time: Thursday\, February 6\, 2025/ 2:30 PM – 3:30 PM (In-Person)\nLocation: See Meeting URL on the cels-seminars website which will require an Argonne login. \nDescription: 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. \nBio: 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’s University Belfast\, Northern Ireland. Nathan’s 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. \nPlease note that the meeting URL for this event can be seen on the cels-seminars website which requires an Argonne login. \n\nSee all upcoming talks at https://www.anl.gov/mcs/lans-seminars \n\n  \n 
URL:https://wordpress.cels.anl.gov/lans-seminars/event/3691/
LOCATION:https://wordpress.cels.anl.gov/cels-seminars/event/lans-seminar-169/
CATEGORIES:Seminar
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