7 papers
Universality of Neural Network Field Theory
Christian Ferko, James Halverson, Aaron Mutchler
We prove that any quantum field theory, or more generally any probability distribution over tempered distributions in , admits a neural network description with a cou…
String Theory from Infinite Width Neural Networks
Samuel Frank, James Halverson
We realize bosonic string theory with ensembles of infinite width neural networks. The string tension is tuned by the variance of the output weights. The construction provides a ne…
F-theory Axiverse
Sebastian Vander Ploeg Fallon, James Halverson, Liam McAllister +1
We compute the couplings of Ramond-Ramond four-form axions in three ensembles of F-theory compactifications, with up to 181,200 axions. We work in the stretched Kähler cone, where…
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…
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…