Asymptotically unbiased estimation of physical observables with neural samplers
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Nicoli, Kim A. | - |
dc.contributor.author | Nakajima, Shinichi | - |
dc.contributor.author | Strodthoff, Nils | - |
dc.contributor.author | Samek, Wojciech | - |
dc.contributor.author | Mueller, Klaus-Robert | - |
dc.contributor.author | Kessel, Pan | - |
dc.date.accessioned | 2021-08-31T10:35:43Z | - |
dc.date.available | 2021-08-31T10:35:43Z | - |
dc.date.created | 2021-06-19 | - |
dc.date.issued | 2020-02-10 | - |
dc.identifier.issn | 2470-0045 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/57678 | - |
dc.description.abstract | We propose a general framework for the estimation of observables with generative neural samplers focusing on modern deep generative neural networks that provide an exact sampling probability. In this framework, we present asymptotically unbiased estimators for generic observables, including those that explicitly depend on the partition function such as free energy or entropy, and derive corresponding variance estimators. We demonstrate their practical applicability by numerical experiments for the two-dimensional Ising model which highlight the superiority over existing methods. Our approach greatly enhances the applicability of generative neural samplers to real-world physical systems. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | AMER PHYSICAL SOC | - |
dc.title | Asymptotically unbiased estimation of physical observables with neural samplers | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Mueller, Klaus-Robert | - |
dc.identifier.doi | 10.1103/PhysRevE.101.023304 | - |
dc.identifier.wosid | 000513236700017 | - |
dc.identifier.bibliographicCitation | PHYSICAL REVIEW E, v.101, no.2 | - |
dc.relation.isPartOf | PHYSICAL REVIEW E | - |
dc.citation.title | PHYSICAL REVIEW E | - |
dc.citation.volume | 101 | - |
dc.citation.number | 2 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Physics | - |
dc.relation.journalWebOfScienceCategory | Physics, Fluids & Plasmas | - |
dc.relation.journalWebOfScienceCategory | Physics, Mathematical | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
(02841) 서울특별시 성북구 안암로 14502-3290-1114
COPYRIGHT © 2021 Korea University. All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.