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UID:VdE5NzxAkfRqwi2NeFWAJ
SUMMARY:Computational Learning for Model Reduction
DTSTAMP:20260401T014400Z
DTSTART;VALUE=DATE:20250106
DTEND;VALUE=DATE:20250110
DESCRIPTION:This workshop showcases emerging frontiers in ROM (reduced orde
	r modeling) by bringing together researchers whose core interests lie in m
	odel reduction and approximation theory\, but who have also explored and d
	eveloped novel methods that utilize various aspects of statistical learnin
	g and data science. Topics of the workshop will include: 1) new mathematic
	al and computational nonlinear ROM formulations required for prediction of
	 transport phenomena\, 2) new model reduction and meta-learning opportunit
	ies necessitated by the prevalence of large neural network models\, 3) new
	 paradigms for ROM capable of attacking parameter inference and ill-posed 
	inverse problems\, 4) new frontiers in the automated learning of latent re
	presentations due to the availability of computationally feasible optimiza
	tion in statistical and machine learning\,  5) an appropriate computationa
	l balancing of observational or experimental data with simulation-based mo
	dels and ROM that would lead to the usage of ROM in digital twin-scale app
	lications\, and 6) challenges and new developments in ROM that aims to pre
	serve inherent physical structure of the underlying dynamics. 
URL:https://icerm.brown.edu/topical_workshops/tw-25-clmr/
LOCATION:Providence\, RI\, USA
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