Variance-reduced random batch Langevin dynamics

The random batch method is advantageous in accelerating force calculations in particle simulations, but it poses a challenge of removing the artificial heating effect in application to the Langevin dynamics. We develop an approach to solve this issue by estimating the force variance, resulting in a variance-reduced random batch Langevin dynamics. Theoretical analysis shows the high-order local truncation error of the time step in the numerical discretization scheme, consistent with the fluctuation-dissipation theorem. The numerical results indicate that the method can achieve a significant variance reduction since a smaller batch size provides accurate approximation, demonstrating the attractive feature of the variance-reduced random batch method for Langevin dynamics.

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Publication Details

Journal
The Journal of Chemical Physics
Published
2024-12-28
DOI
https://doi.org/10.1063/5.0246661
Citations
1
Primary Topic
Markov Chains and Monte Carlo Methods
Type
article
Field-Weighted Citation Impact
0.71

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article

Variance-reduced random batch Langevin dynamics

Yue Zhao, Zhenli Xu, Qi Zhou
1 citations
The Journal of Chemical Physics
Markov Chains and Monte Carlo Methods
0.71
article

Variance-reduced random batch Langevin dynamics

Yue Zhao, Zhenli Xu, Qi Zhou
article en
1 citations

Abstract

The random batch method is advantageous in accelerating force calculations in particle simulations, but it poses a challenge of removing the artificial heating effect in application to the Langevin dynamics. We develop an approach to solve this issue by estimating the force variance, resulting in a variance-reduced random batch Langevin dynamics. Theoretical analysis shows the high-order local truncation error of the time step in the numerical discretization scheme, consistent with the fluctuation-dissipation theorem. The numerical results indicate that the method can achieve a significant variance reduction since a smaller batch size provides accurate approximation, demonstrating the attractive feature of the variance-reduced random batch method for Langevin dynamics.

The Journal of Chemical PhysicsVol. 161(24)
Shanghai Jiao Tong University (CN), Michigan State University (US)
National Natural Science Foundation of China, Science and Technology Commission of Shanghai Municipality
Openalex Percentile: Top 23%
Markov Chains and Monte Carlo Methods
0.71
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