Mitigating topological freezing using out-of-equilibrium simulations
Motivated by the recently-established connection between Jarzynski’s equality and the theoretical framework of Stochastic Normalizing Flows, we investigate a protocol relying on out-of-equilibrium lattice Monte Carlo simulations to mitigate the infamous computational problem of topological freezing....
| Autores: | , , |
|---|---|
| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2024 |
| País: | España |
| Institución: | Consejo Superior de Investigaciones Científicas (CSIC) |
| Repositorio: | DIGITAL.CSIC. Repositorio Institucional del CSIC |
| OAI Identifier: | oai:digital.csic.es:10261/414113 |
| Acceso en línea: | http://hdl.handle.net/10261/414113 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85191183396&doi=10.1007%2FJHEP04%282024%29126&partnerID=40&md5=9e3e7869a29de8b56a472d47950b837f |
| Access Level: | acceso abierto |
| Palabra clave: | Algorithms and Theoretical Developments Lattice Quantum Field Theory Other Lattice Field Theories Vacuum Structure and Confinement |
| Sumario: | Motivated by the recently-established connection between Jarzynski’s equality and the theoretical framework of Stochastic Normalizing Flows, we investigate a protocol relying on out-of-equilibrium lattice Monte Carlo simulations to mitigate the infamous computational problem of topological freezing. We test our proposal on 2d CPN−1 models and compare our results with those obtained adopting the Parallel Tempering on Boundary Conditions proposed by M. Hasenbusch, obtaining comparable performances. Our work thus sets the stage for future applications combining our Monte Carlo setup with machine learning techniques. © The Author(s) 2024. |
|---|