| Autor(es): |
Freitas, William
;
Vitiello, S. A.
Número total de Autores: 2
|
| Tipo de documento: | Artigo Científico |
| Fonte: | QUANTUM; v. N/A, p. 9-pg., 2023-12-01. |
| Resumo | |
We introduce a neural network-based approach for modeling wave functions that satisfy Bose-Einstein statistics. Applying this ing from 2 to 14 atoms), we accurately predict ground state energies, pair density functions, and two-body contact parameters C(N) 2 related to weak unitarity. The results obtained via the variational Monte Carlo method exhibit remarkable agreement with previous studies using the diffusion Monte Carlo method, which is considered exact within its statistical uncertainties. This indicates the effectiveness of our neural network approach for investigating many-body systems governed by Bose-Einstein statistics. (AU) | |
| Processo FAPESP: | 20/10505-6 - Abordagem de deep learning para física de muitos corpos quânticos |
| Beneficiário: | William Freitas e Silva |
| Modalidade de apoio: | Bolsas no Brasil - Doutorado |
| Processo FAPESP: | 16/17612-7 - Dinâmica de sistemas de muitos corpos IV |
| Beneficiário: | Arnaldo Gammal |
| Modalidade de apoio: | Auxílio à Pesquisa - Temático |