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PRODID:adamgibbons/ics
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UID:-jk4KURIG-shKzBJ6IHdj
SUMMARY:Scientific Machine Learning for Gravitational Wave Astronomy
DTSTAMP:20260330T195000Z
DTSTART;VALUE=DATE:20250602
DTEND;VALUE=DATE:20250606
DESCRIPTION:The aim of this workshop is to bring together participants from
	 computational mathematics and gravitational wave astronomy to tackle comp
	utational challenges in leveraging data-driven methods in key areas of gra
	vitational wave data analysis in order to maximize the science output of t
	he ongoing and upcoming observations. The areas of focus will be: (i) nois
	e classification and detection\, (ii) waveform modeling and uncertainty qu
	antification\, and (iii) source parameter and astrophysical population Bay
	esian inference.
URL:https://icerm.brown.edu/program/topical_workshop/tw-25-smlgwa
LOCATION:Institute for Computational and Experimental Research in Mathemati
	cs\, Brown University 
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