Non UBC
DSpace
Fabian Ruehle
2019-10-31T08:17:43Z
2019-05-03T09:00
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.
https://circle.library.ubc.ca/rest/handle/2429/72125?expand=metadata
33.0 minutes
video/mp4
Author affiliation: CERN
Oaxaca (Mexico : State)
10.14288/1.0384826
eng
Unreviewed
Vancouver : University of British Columbia Library
Banff International Research Station for Mathematical Innovation and Discovery
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
Postdoctoral
BIRS Workshop Lecture Videos (Oaxaca (Mexico : State))
Mathematics
Relativity and gravitational theory
Quantum theory
String theory
Machine Learning for string vacua
Moving Image
http://hdl.handle.net/2429/72125