From the String Landscape to the Mathematical Landscape: a Machine-Learning Outlook
High Energy Physics - Theory
2022-02-15 v1 Machine Learning
History and Overview
Abstract
We review the recent programme of using machine-learning to explore the landscape of mathematical problems. With this paradigm as a model for human intuition - complementary to and in contrast with the more formalistic approach of automated theorem proving - we highlight some experiments on how AI helps with conjecture formulation, pattern recognition and computation.
Keywords
Cite
@article{arxiv.2202.06086,
title = {From the String Landscape to the Mathematical Landscape: a Machine-Learning Outlook},
author = {Yang-Hui He},
journal= {arXiv preprint arXiv:2202.06086},
year = {2022}
}
Comments
10 pages, 2 figures. Based on various talks in 2021-22, this is an invited contribution to the Proceedings of "The 14th International Workshop on Lie theory and its applications in physics", to be published by Springer-Nature