Parametric Matrix Models: Equations as Data
ORAL · Invited
Abstract
A picture is worth a thousand words, and an equation is worth a thousand data points. Recent work established Parametric Matrix Models (PMMs) as a powerful and unique machine learning tool designed specifically to take advantage of the information contained within the governing equations of physical systems. This talk will include an introduction to PMM methods, the state of research regarding PMMs, and various applications of PMMs for the emulation of nuclear systems.
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Presenters
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Patrick Cook
Michigan State University, Facility for Rare Isotope Beams, Michigan State University
Authors
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Patrick Cook
Michigan State University, Facility for Rare Isotope Beams, Michigan State University
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Danny Jammooa
Facility for Rare Isotope Beams, Michigan State University, Michigan State University
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Morten Hjorth-Jensen
University of Oslo, Facility for Rare Isotope Beams, Michigan State University
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Dean J Lee
Facility for Rare Isotope Beams, Michigan State University, Michigan State University