533 citations · 1.7k across the 63 of their papers we have counts for
70 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 Álvarez-García, 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 Álvarez-García, 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…
Improving Generative Inverse Design of Rectangular Patch Antennas with Test Time Optimization
Beck LaBash, Shahriar Khushrushahi, Fabian Ruehle
We propose a two-stage deep learning framework for the inverse design of rectangular patch antennas. Our approach leverages generative modeling to learn a latent representation of…
Symbolic Regression with Multimodal Large Language Models and Kolmogorov Arnold Networks
Thomas R. Harvey, Fabian Ruehle, Kit Fraser-Taliente +1
We present a novel approach to symbolic regression using vision-capable large language models (LLMs) and the ideas behind Google DeepMind's Funsearch. The LLM is given a plot of a…
Learning Topological Invariance
James Halverson, Fabian Ruehle
Two geometric spaces are in the same topological class if they are related by certain geometric deformations. We propose machine learning methods that automate learning of topologi…
On the Learnability of Knot Invariants: Representation, Predictability, and Neural Similarity
Audrey Lindsay, Fabian Ruehle
We analyze different aspects of neural network predictions of knot invariants. First, we investigate the impact of different knot representations on the prediction of invariants an…
Interpretable Machine Learning for Kronecker Coefficients
Giorgi Butbaia, Kyu-Hwan Lee, Fabian Ruehle
We analyze the saliency of neural networks and employ interpretable machine learning models to predict whether the Kronecker coefficients of the symmetric group are zero or not. Ou…
Data for Mathematical Copilots: Better Ways of Presenting Proofs for Machine Learning
Simon Frieder, Jonas Bayer, Sam Looi +13
The datasets and benchmarks commonly used to train and evaluate the mathematical capabilities of AI-based mathematical copilots (primarily large language models) exhibit several sh…
A Twist on Heterotic Little String Duality
Hamza Ahmed, Paul-Konstantin Oehlmann, Fabian Ruehle
In this work, we significantly expand the web of T-dualities among heterotic NS5-brane theories with eight supercharges. This is achieved by introducing twists involving outer auto…
Attractors, Geodesics, and the Geometry of Moduli Spaces
Fabian Ruehle, Benjamin Sung
We connect recent conjectures and observations pertaining to geodesics, attractor flows, Laplacian eigenvalues and the geometry of moduli spaces by using that attractor flows are g…
A Heterotic Kähler Gravity and the Distance Conjecture
Javier José Murgas Ibarra, Paul-Konstantin Oehlmann, Fabian Ruehle +1
Deformations of the heterotic superpotential give rise to a topological holomorphic theory with similarities to both Kodaira-Spencer gravity and holomorphic Chern-Simons theory. Al…
Bounds and Dualities of Type II Little String Theories
Florent Baume, Paul-Konstantin Oehlmann, Fabian Ruehle
We explore the symmetry structure of Type II Little String Theories and their T-dualities. We construct these theories both from the bottom-up perspective starting with seed Superc…
The Frozen Phase of Heterotic F-theory Duality
Paul-Konstantin Oehlmann, Fabian Ruehle, Benjamin Sung
We study the duality between the Spin heterotic string without vector structure and F-theory with frozen singularities. We give a complete description in theorie…
KAN: Kolmogorov-Arnold Networks
Ziming Liu, Yixuan Wang, Sachin Vaidya +5
Inspired by the Kolmogorov-Arnold representation theorem, we propose Kolmogorov-Arnold Networks (KANs) as promising alternatives to Multi-Layer Perceptrons (MLPs). While MLPs have…
On classical de Sitter solutions and parametric control
David Andriot, Fabian Ruehle
Finding string backgrounds with de Sitter spacetime, where all approximations and corrections are controlled, is an open problem. We revisit the search for de Sitter solutions in t…
Rigor with Machine Learning from Field Theory to the Poincaré Conjecture
Sergei Gukov, James Halverson, Fabian Ruehle
Machine learning techniques are increasingly powerful, leading to many breakthroughs in the natural sciences, but they are often stochastic, error-prone, and blackbox. How, then, s…
T-Duality and Flavor Symmetries in Little String Theories
Hamza Ahmed, Paul-Konstantin Oehlmann, Fabian Ruehle
We explore the T-duality web of 6D Heterotic Little String Theories, focusing on flavor algebra reducing deformations. A careful analysis of the full flavor algebra, including Abel…
Metric Flows with Neural Networks
James Halverson, Fabian Ruehle
We develop a general theory of flows in the space of Riemannian metrics induced by neural network gradient descent. This is motivated in part by recent advances in approximating Ca…
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…
Level Crossings, Attractor Points and Complex Multiplication
Hamza Ahmed, Fabian Ruehle
We study the complex structure moduli dependence of the scalar Laplacian eigenmodes for one-parameter families of Calabi-Yau -folds in P^{n+1}. It was previously observed that s…
Symmetries of Calabi-Yau Prepotentials with Isomorphic Flops
Andre Lukas, Fabian Ruehle
Calabi-Yau threefolds with infinitely many flops to isomorphic manifolds have an extended Kahler cone made up from an infinite number of individual Kahler cones. These cones are re…