Evolving heterotic gauge backgrounds: genetic algorithms versus reinforcement learning
The immensity of the string landscape and the difficulty of identifying solutions that match the observed features of particle physics have raised serious questions about the predictive power of string theory. Modern methods of optimisation and search can, however, significantly improve the prospect...
Главные авторы: | Abel, S, Constantin, A, Harvey, TR, Lukas, A |
---|---|
Формат: | Journal article |
Язык: | English |
Опубликовано: |
Wiley
2022
|
Схожие документы
-
Heterotic string model building with monad bundles and reinforcement learning
по: Constantin, A, и др.
Опубликовано: (2022) -
Heterotic string model building with monad bundles and reinforcement learning
по: Constantin, A, и др.
Опубликовано: (2021) -
String model building, reinforcement learning and genetic algorithms
по: Abel, S, и др.
Опубликовано: (2021) -
Decoding nature with nature’s tools: heterotic line bundle models of particle physics with genetic algorithms and quantum annealing
по: Abel, SA, и др.
Опубликовано: (2023) -
Gauge five-brane moduli in four-dimensional heterotic models
по: Gray, J, и др.
Опубликовано: (2004)