TY - ELEC
AU - Fabian Ruehle
PY - 2019
TI - Machine Learning for string vacua
LA - eng
M3 - Moving Image
AB - 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.
N2 - 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.
UR - https://open.library.ubc.ca/collections/48630/items/1.0384826
ER - End of Reference