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Found 8 papers, 3 papers with code
Rigor with Machine Learning from Field Theory to the Poincaré Conjecture
Machine learning techniques are increasingly powerful, leading to many breakthroughs in the natural sciences, but they are often stochastic, error-prone, and blackbox.
Metric Flows with Neural Networks
We develop a theory of flows in the space of Riemannian metrics induced by neural network gradient descent.
Searching for ribbons with machine learning
We apply Bayesian optimization and reinforcement learning to a problem in topology: the question of when a knot bounds a ribbon disk.
Learning Size and Shape of Calabi-Yau Spaces
We present a new machine learning library for computing metrics of string compactification spaces.
Learning to Unknot
We introduce natural language processing into the study of knot theory, as made natural by the braid word representation of knots.
Machine Learning and Algebraic Approaches towards Complete Matter Spectra in 4d F-theory
To quantify jumps of these cohomologies, we first generate 1. 8 million pairs of line bundles and curves embedded in $dP_3$, for which we compute the cohomologies.
High Energy Physics - Theory Algebraic Geometry
Branes with Brains: Exploring String Vacua with Deep Reinforcement Learning
In one case, we demonstrate that the agent learns a human-derived strategy for finding consistent string models.
High Energy Physics - Theory
Dark Glueballs and their Ultralight Axions
Dark gauge sectors and axions are well-motivated in string theory.
High Energy Physics - Phenomenology High Energy Physics - Theory
