A Machine Learning Approach to Trapped Many-Fermion Systems
Fuente:
arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866909359725871104 |
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| author | Bedaque, Paulo F. Kumar, Hersh Sheng, Andy |
| author_facet | Bedaque, Paulo F. Kumar, Hersh Sheng, Andy |
| contents | 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". |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_17383 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Machine Learning Approach to Trapped Many-Fermion Systems Bedaque, Paulo F. Kumar, Hersh Sheng, Andy Nuclear Theory Disordered Systems and Neural Networks Quantum Physics 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". |
| title | A Machine Learning Approach to Trapped Many-Fermion Systems |
| topic | Nuclear Theory Disordered Systems and Neural Networks Quantum Physics |
| url | https://arxiv.org/abs/2410.17383 |