Status: Bibliographieeintrag
Standort: ---
Exemplare:
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| Online-Ressource |
Verfasst von: | Urban, Julian M. [VerfasserIn]  |
| Pawlowski, Jan M. [VerfasserIn]  |
Titel: | Reducing autocorrelation times in lattice simulations with generative adversarial networks |
Verf.angabe: | Julian M. Urban and Jan M. Pawlowski |
E-Jahr: | 2018 |
Jahr: | 8 Nov 2018 |
Umfang: | 9 S. |
Fussnoten: | Gesehen am 14.12.2018 |
Titel Quelle: | Enthalten in: De.arxiv.org |
Ort Quelle: | [S.l.] : Arxiv.org, 1991 |
Jahr Quelle: | 2018 |
Band/Heft Quelle: | (2018), Artikel-ID 1811.03533 |
Abstract: | Short autocorrelation times are essential for a reliable error assessment in Monte Carlo simulations of lattice systems. A generative adversarial network (GAN) can provide independent samples, thereby eliminating autocorrelations in the Markov chain. We address the question of statistical accuracy by implementing GANs as an overrelaxation step, incorporated into a traditional hybrid Monte Carlo algorithm. This allows for a sensible numerical assessment of ergodicity and consistency. Results for scalar $\phi^4$-theory in two dimensions are presented. We achieve a significant reduction of autocorrelations while accurately reproducing the correct statistics. We discuss possible improvements as well as solutions to persisting issues and outline strategies towards the application to gauge theory and critical slowing down. |
URL: | Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.
Volltext: http://arxiv.org/abs/1811.03533 |
Datenträger: | Online-Ressource |
Sprache: | eng |
Sach-SW: | High Energy Physics - Lattice |
| Physics - Computational Physics |
K10plus-PPN: | 1585261726 |
Verknüpfungen: | → Sammelwerk |
Reducing autocorrelation times in lattice simulations with generative adversarial networks / Urban, Julian M. [VerfasserIn]; 8 Nov 2018 (Online-Ressource)
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