Conditional Normalizing flow for Monte Carlo sampling in lattice scalar field theory
Ankur Singha,Dipankar Chakrabarti,Vipul Arora +2 more
- 03 Jul 2022
2
TL;DR: In this article , the authors proposed a conditional normalizing flow (C-NF) model for sampling lattice configurations in the critical region to solve the problem of critical slowing down.
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Abstract: The cost of Monte Carlo sampling of lattice configurations is very high in the critical region of lattice field theory due to the high correlation between the samples. This paper suggests a Conditional Normalizing Flow (C-NF) model for sampling lattice configurations in the critical region to solve the problem of critical slowing down. We train the C-NF model using samples generated by Hybrid Monte Carlo (HMC) in non-critical regions with low simulation costs. The trained C-NF model is employed in the critical region to build a Markov chain of lattice samples with negligible autocorrelation. The C-NF model is used for both interpolation and extrapolation to the critical region of lattice theory. Our proposed method is assessed using the 1+1-dimensional scalar φ 4 theory. This approach enables the construction of lattice ensembles for many parameter values in the critical region, which reduces simulation costs by avoiding the critical slowing down.
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Citations
Parallel tempered metadynamics: Overcoming potential barriers without surfing or tunneling
Timo Eichhorn,Gianluca Fuwa,Christian Hoelbling,Lukas Varnhorst +3 more
TL;DR: Metadynamics combined with parallel tempering successfully unfreezes topological sectors in gauge theories, significantly reducing autocorrelation times. However, the computational overhead is significant in pure gauge theory and the required reweighting procedure may considerably reduce the effective sample size.
Parallel Tempered Metadynamics: Overcoming potential barriers without surfing or tunneling
Tim Rolf Eichhorn,C. Hoelbling,L. Varnhorst +2 more
- 10 Jul 2023
TL;DR: In this paper , the authors demonstrate that for a relevant set of parameters considered, Metadynamics can be used to reduce the autocorrelation times of topological quantities in 4-dimensional SU(3) gauge theory by at least two orders of magnitude compared to conventional update algorithms.
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