Applications of Machine Learning to Lattice Quantum Field Theory
High Energy Physics - Lattice
2022-02-15 v1 Machine Learning
High Energy Physics - Phenomenology
Abstract
There is great potential to apply machine learning in the area of numerical lattice quantum field theory, but full exploitation of that potential will require new strategies. In this white paper for the Snowmass community planning process, we discuss the unique requirements of machine learning for lattice quantum field theory research and outline what is needed to enable exploration and deployment of this approach in the future.
Keywords
Cite
@article{arxiv.2202.05838,
title = {Applications of Machine Learning to Lattice Quantum Field Theory},
author = {Denis Boyda and Salvatore Calì and Sam Foreman and Lena Funcke and Daniel C. Hackett and Yin Lin and Gert Aarts and Andrei Alexandru and Xiao-Yong Jin and Biagio Lucini and Phiala E. Shanahan},
journal= {arXiv preprint arXiv:2202.05838},
year = {2022}
}
Comments
10 pages, contribution to Snowmass 2022