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PRODID:-//University of Utah Math Department//On two problems in machine learning for dynamical systems: learning interaction laws in particle systems, and digital twins in cardiac electrophysiology//EN
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X-WR-CALNAME:On two problems in machine learning for dynamical systems: learning interaction laws in particle systems, and digital twins in cardiac electrophysiology
X-WR-CALDESC:On two problems in machine learning for dynamical systems: learning interaction laws in particle systems, and digital twins in cardiac electrophysiology at University of Utah Mathematics Department
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UID:20250403T160000-mauro-maggioni@math.utah.edu
DTSTART;TZID=America/Denver:20250403T160000
DTEND;TZID=America/Denver:20250403T170000
DTSTAMP:20260922T150858Z
SUMMARY:On two problems in machine learning for dynamical systems: learning interaction laws in particle systems, and digital twins in cardiac electrophysiology
DESCRIPTION:Speaker: Mauro Maggioni\, Johns Hopkins University\n\nI will discuss recent results in two research directions at the intersection of statistical learning and modeling of dynamical systems. First, we consider systems of interacting agents or particles, which are commonly used in models throughout the sciences, and can exhibit complex, emergent …

LOCATION:JWB 335

URL:https://www.math.utah.edu/research/colloquia/2025-04-03-mauro-maggioni/
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