We use reinforcement learning as a means of constructing string compactifications with prescribed properties. Specifically, we study heterotic (Formula presented.) GUT models on Calabi-Yau three-folds with monad bundles, in search of phenomenologically promising examples. Due to the vast number of bundles and the sparseness of viable choices, methods based on systematic scanning are not suitable for this class of models. By focusing on two specific manifolds with Picard numbers two and three, we show that reinforcement learning can be used successfully to explore monad bundles. Training can be accomplished with minimal computing resources and leads to highly efficient policy networks. They produce phenomenologically promising states for nea...
We review the recent programme undertaken ∗ to construct, systematically and algorith-mically, large...
We systematically approach the construction of heterotic E8 ×E8 Calabi-Yau models, based on compact ...
We propose deep reinforcement learning as a model-free method for exploring the landscape of string ...
We use reinforcement learning as a means of constructing string compactifications with prescribed pr...
We use deep reinforcement learning to explore a class of heterotic SU(5) GUT models constructed from...
We study machine learning of phenomenologically relevant properties of string compactifications, whi...
We study machine learning of phenomenologically relevant properties of string compactifications, whi...
We study machine learning of phenomenologically relevant properties of string compactifications, whi...
We study machine learning of phenomenologically relevant properties of string compactifications, whi...
We study machine learning of phenomenologically relevant properties of string compactifications, whi...
The goal of this thesis is to review and investigate recent applications of machine learning to prob...
The goal of this thesis is to review and investigate recent applications of machine learning to prob...
The immensity of the string landscape and the difficulty of identifying solutions that match the obs...
We undertake a systematic scan of vector bundles over spaces from the largest database of known Cala...
This thesis contributes with a number of topics to the subject of string compactifications, especial...
We review the recent programme undertaken ∗ to construct, systematically and algorith-mically, large...
We systematically approach the construction of heterotic E8 ×E8 Calabi-Yau models, based on compact ...
We propose deep reinforcement learning as a model-free method for exploring the landscape of string ...
We use reinforcement learning as a means of constructing string compactifications with prescribed pr...
We use deep reinforcement learning to explore a class of heterotic SU(5) GUT models constructed from...
We study machine learning of phenomenologically relevant properties of string compactifications, whi...
We study machine learning of phenomenologically relevant properties of string compactifications, whi...
We study machine learning of phenomenologically relevant properties of string compactifications, whi...
We study machine learning of phenomenologically relevant properties of string compactifications, whi...
We study machine learning of phenomenologically relevant properties of string compactifications, whi...
The goal of this thesis is to review and investigate recent applications of machine learning to prob...
The goal of this thesis is to review and investigate recent applications of machine learning to prob...
The immensity of the string landscape and the difficulty of identifying solutions that match the obs...
We undertake a systematic scan of vector bundles over spaces from the largest database of known Cala...
This thesis contributes with a number of topics to the subject of string compactifications, especial...
We review the recent programme undertaken ∗ to construct, systematically and algorith-mically, large...
We systematically approach the construction of heterotic E8 ×E8 Calabi-Yau models, based on compact ...
We propose deep reinforcement learning as a model-free method for exploring the landscape of string ...