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| Hauptseite > Publikationsdatenbank > From Theory to Practice: Applying Neural Networks to Simulate Real Systems with Sign Problems > print |
| Hauptseite > Publikationsdatenbank > From Theory to Practice: Applying Neural Networks to Simulate Real Systems with Sign Problems > print |
| 001 | 1019471 | ||
| 005 | 20240116205230.0 | ||
| 024 | 7 | _ | |a arXiv:2311.18312 |2 arXiv |
| 024 | 7 | _ | |a 10.34734/FZJ-2023-05418 |2 datacite_doi |
| 037 | _ | _ | |a FZJ-2023-05418 |
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| 100 | 1 | _ | |a Rodekamp, Marcel |0 P:(DE-Juel1)185942 |b 0 |e Corresponding author |u fzj |
| 111 | 2 | _ | |a The 40th International Symposium on Lattice Field Theory |g LATTICE2023 |c Batavia, Illinois |d 2023-07-31 - 2023-08-04 |w USA |
| 245 | _ | _ | |a From Theory to Practice: Applying Neural Networks to Simulate Real Systems with Sign Problems |
| 260 | _ | _ | |a Trieste |c 2023 |b SISSA |
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| 520 | _ | _ | |a The numerical sign problem poses a seemingly insurmountable barrier to the simulation of many fascinating systems. We apply neural networks to deform the region of integration, mitigating the sign problem of systems with strongly correlated electrons. In this talk we present our latest architectural developments as applied to contour deformation. We also demonstrate its applicability to real systems, namely perylene. |
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| 773 | _ | _ | |y 2023 |0 PERI:(DE-600)2642026-0 |v 453 |t Proceedings of Science / International School for Advanced Studies |x 1824-8039 |
| 856 | 4 | _ | |u https://arxiv.org/abs/2311.18312v1 |
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