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PRODID:-//University of Utah Math Department//Shallow Recurrent Decoders for the Automated Discovery of Physical Models//EN
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X-WR-CALNAME:Shallow Recurrent Decoders for the Automated Discovery of Physical Models
X-WR-CALDESC:Shallow Recurrent Decoders for the Automated Discovery of Physical Models at University of Utah Mathematics Department
X-WR-TIMEZONE:America/Denver
BEGIN:VEVENT
UID:20251023T160000-nathan-kutz@math.utah.edu
DTSTART;TZID=America/Denver:20251023T160000
DTEND;TZID=America/Denver:20251023T170000
DTSTAMP:20260922T150858Z
SUMMARY:Shallow Recurrent Decoders for the Automated Discovery of Physical Models
DESCRIPTION:Speaker: Nathan Kutz\, University of Washington\n\nA major challenge in the study of science and engineering systems is that of model discovery: turning data into dynamical models that are not just predictive, but provide insight into the nature of the underlying physics and dynamics that generated the data. We introduce a number of data-driven …

LOCATION:JWB 335

URL:https://www.math.utah.edu/research/colloquia/2025-10-23-nathan-kutz/
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