Title	Machine Learning for string vacua
Creator	Ruehle, Fabian
Publisher	Banff International Research Station for Mathematical Innovation and Discovery
Date Issued	2019-05-03T09:00
Description	In string theory, we face a gigantic number of backgrounds, each of which comes with different implications for particle physics and cosmology. On top of this, every backgrounds has a huge number of possible vacua or near-vacua. We describe the computational complexity of the challenges associated with both finding a viable background and finding vacua for this background, and apply machine learning to study a small subset of them.
Extent	33.0 minutes
Subject	Mathematics; Relativity and gravitational theory; Quantum theory; String theory
Type	Moving Image
File Format	video/mp4
Language	eng
Notes	Author affiliation: CERN
Series	BIRS Workshop Lecture Videos (Oaxaca de Juárez (Mexico))
Date Available	2019-10-31
Provider	Vancouver : University of British Columbia Library
Rights	Attribution-NonCommercial-NoDerivatives 4.0 International
DOI	10.14288/1.0384826
URI	http://hdl.handle.net/2429/72125
Affiliation	Non UBC
Peer Review Status	Unreviewed
Scholarly Level	Postdoctoral
Rights URI	http://creativecommons.org/licenses/by-nc-nd/4.0/
Aggregated Source Repository	DSpace

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