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Found 11 papers, 1 papers with code
Learning to be Simple
In this work we employ machine learning to understand structured mathematical data involving finite groups and derive a theorem about necessary properties of generators of finite simple groups.
Machine Learned Calabi-Yau Metrics and Curvature
Finding Ricci-flat (Calabi-Yau) metrics is a long standing problem in geometry with deep implications for string theory and phenomenology.
Topological data analysis on noisy quantum computers
In this study, we present NISQ-TDA, a fully implemented end-to-end quantum machine learning algorithm needing only a short circuit-depth, that is applicable to high-dimensional classical data, and with provable asymptotic speedup for certain classes of problems.
Identifying equivalent Calabi--Yau topologies: A discrete challenge from math and physics for machine learning
We review briefly the characteristic topological data of Calabi--Yau threefolds and focus on the question of when two threefolds are equivalent through related topological data.
Machine Learning Kreuzer--Skarke Calabi--Yau Threefolds
Using a fully connected feedforward neural network we study topological invariants of a class of Calabi--Yau manifolds constructed as hypersurfaces in toric varieties associated with reflexive polytopes from the Kreuzer--Skarke database.
Learning knot invariants across dimensions
We find that a two-layer feed-forward neural network can predict $s$ from $\text{Kh}(q,-q^{-4})$ with greater than $99\%$ accuracy.
Neural Network Approximations for Calabi-Yau Metrics
Ricci flat metrics for Calabi-Yau threefolds are not known analytically.
Disentangling a Deep Learned Volume Formula
We present a simple phenomenological formula which approximates the hyperbolic volume of a knot using only a single evaluation of its Jones polynomial at a root of unity.
Baryons from Mesons: A Machine Learning Perspective
Quantum chromodynamics (QCD) is the theory of the strong interaction.
Dynamics of genetic code evolution: The emergence of universality
We study the dynamics of genetic code evolution.
Machine Learning CICY Threefolds
The latest techniques from Neural Networks and Support Vector Machines (SVM) are used to investigate geometric properties of Complete Intersection Calabi-Yau (CICY) threefolds, a class of manifolds that facilitate string model building.
