A Machine Learning Approach to Trapped Many-Fermion Systems
Nuclear Theory
2024-10-24 v1 Disordered Systems and Neural Networks
Quantum Physics
Abstract
We apply a variational Ansatz based on neural networks to the problem of spin- fermions in a harmonic trap interacting through a short distance potential. We showed that standard machine learning techniques lead to a quick convergence to the ground state, especially in weakly coupled cases. Higher couplings can be handled efficiently by increasing the strength of interactions during "training".
Cite
@article{arxiv.2410.17383,
title = {A Machine Learning Approach to Trapped Many-Fermion Systems},
author = {Paulo F. Bedaque and Hersh Kumar and Andy Sheng},
journal= {arXiv preprint arXiv:2410.17383},
year = {2024}
}
Comments
8 pages, 5 figures