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arXiv · 2410.17383

A Machine Learning Approach to Trapped Many-Fermion Systems

Paulo F. Bedaque·Hersh Kumar·Andy Sheng
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Abstract

We apply a variational Ansatz based on neural networks to the problem of spin-$1/2$ 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".

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Paulo F. Bedaque, Hersh Kumar, Andy Sheng. 2024-10-22. A Machine Learning Approach to Trapped Many-Fermion Systems. https://arxiv.org/abs/2410.17383

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