Detecting and mitigating mode-collapse for flow-based sampling of lattice field theories-Reference-Cited by-同舟云学术

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Detecting and mitigating mode-collapse for flow-based sampling of lattice field theories

Published:2023-12-08 Issue:11 Volume:108 Page:
ISSN:2470-0010
Container-title:Physical Review D
language:en
Short-container-title:Phys. Rev. D

Author:

Nicoli Kim A.^ORCID,Anders Christopher J.^ORCID,Hartung Tobias^ORCID,Jansen Karl,Kessel Pan,Nakajima Shinichi^ORCID

Funder

Bundesministerium für Bildung und Forschung

HORIZON EUROPE Framework Programme

H2020 Marie Skłodowska-Curie Actions

Einstein Research Unit Quantum

Ministry of Science, Research, and Culture of the State of Brandenburg

Publisher

American Physical Society (APS)

Link

http://harvest.aps.org/v2/journals/articles/10.1103/PhysRevD.108.114501/fulltext

Reference51 articles.

1. Flow-based generative models for Markov chain Monte Carlo in lattice field theory

2. Estimation of Thermodynamic Observables in Lattice Field Theories with Deep Generative Models

3. Machine Learning of Thermodynamic Observables in the Presence of Mode Collapse

4. Stochastic normalizing flows as non-equilibrium transformations

5. Stochastic normalizing flows for lattice field theory

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1. A comparative lattice analysis of SU(2) dark glueballs*;Chinese Physics C;2024-08-01

2. Parallel tempered metadynamics: Overcoming potential barriers without surfing or tunneling;Physical Review D;2024-06-07

3. Applications of flow models to the generation of correlated lattice QCD ensembles;Physical Review D;2024-05-24

4. Diffusion models as stochastic quantization in lattice field theory;Journal of High Energy Physics;2024-05-07

5. Sampling the lattice Nambu-Goto string using Continuous Normalizing Flows;Journal of High Energy Physics;2024-02-08

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