10 papers
Learning Topological Features of -invariants
Brandon Robinson, Shimal Harichurn, Fabian Ruehle +3
Machine learning and data analysis techniques have recently emerged as powerful tools for identifying patterns and formulating conjectures in mathematical research, most notably in…
Warped Numerical Calabi-Yau Metrics
Severin Lüst, Fabian Ruehle, Simon Schreyer
We compute numerical warped Type IIB flux backgrounds on Calabi-Yau threefolds following the construction of Giddings, Kachru, and Polchinski. Using physics-informed neural network…
Kaleidoscopes, Waves and the Prepotential
Rafael Ãlvarez-GarcÃa, Fabian Ruehle
Isomorphic flops are topology-changing transitions connecting two diffeomorphic families of Calabi-Yau threefolds. They correspond to the generators of certain Coxeter groups actin…
Harmonic Analysis of the Instanton Prepotential
Rafael Ãlvarez-GarcÃa, Fabian Ruehle
Discrete symmetries of Calabi-Yau moduli spaces, generated by isomorphic flops, constrain the instanton expansion of the 4D Type~IIA prepotential. We show that the…
Fermions and Supersymmetry in Neural Network Field Theories
Samuel Frank, James Halverson, Anindita Maiti +1
We introduce fermionic neural network field theories via Grassmann-valued neural networks. Free theories are obtained by a generalization of the Central Limit Theorem to Grassmann…
Searching for ribbons with machine learning
Sergei Gukov, James Halverson, Ciprian Manolescu +1
We apply Bayesian optimization and reinforcement learning to a problem in topology: the question of when a knot bounds a ribbon disk. This question is relevant in an approach to di…