The velocity jump Langevin process and its splitting scheme: long time convergence and numerical accuracy

The Langevin dynamics is a diffusion process extensively used, in particular in molecular dynamics simulations, to sample Gibbs measures. Some alternatives based on (piecewise deterministic) kinetic velocity jump processes have gained interest over the last decade. One interest of the latter is the possibility to split forces (at the continuous-time level), reducing the numerical cost for sampling the trajectory. Motivated by this, a numerical scheme based on hybrid dynamics combining velocity jumps and Langevin diffusion, numerically more efficient than their classical Langevin counterparts, has been introduced for computational chemistry in [43]. The present work is devoted to the numerical analysis of this scheme. Our main results are, first, the exponential ergodicity of the continuous-time velocity jump Langevin process, second, a Talay–Tubaro expansion of the invariant measure of the numerical scheme on the torus, showing in particular that the scheme is of weak order <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mn>2</mml:mn> </mml:math> in the step-size and, third, a bound on the quadratic risk of the corresponding practical MCMC estimator (possibly with Richardson extrapolation). With respect to previous works on the Langevin diffusion, new difficulties arise from the jump operator, which is non-local.

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Publication Details

Journal
SMAI Journal of Computational Mathematics
Published
2025-09-25
DOI
https://doi.org/10.5802/smai-jcm.134
Primary Topic
Advanced MRI Techniques and Applications
Type
article
Field-Weighted Citation Impact
0.00

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article

The velocity jump Langevin process and its splitting scheme: long time convergence and numerical accuracy

Lucas Journel, Pierre Monmarché, Nicolaï Gouraud
SMAI Journal of Computational Mathematics
Advanced MRI Techniques and Applications
article

The velocity jump Langevin process and its splitting scheme: long time convergence and numerical accuracy

Lucas Journel, Pierre Monmarché, Nicolaï Gouraud
article en

Abstract

The Langevin dynamics is a diffusion process extensively used, in particular in molecular dynamics simulations, to sample Gibbs measures. Some alternatives based on (piecewise deterministic) kinetic velocity jump processes have gained interest over the last decade. One interest of the latter is the possibility to split forces (at the continuous-time level), reducing the numerical cost for sampling the trajectory. Motivated by this, a numerical scheme based on hybrid dynamics combining velocity jumps and Langevin diffusion, numerically more efficient than their classical Langevin counterparts, has been introduced for computational chemistry in [43]. The present work is devoted to the numerical analysis of this scheme. Our main results are, first, the exponential ergodicity of the continuous-time velocity jump Langevin process, second, a Talay–Tubaro expansion of the invariant measure of the numerical scheme on the torus, showing in particular that the scheme is of weak order <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mn>2</mml:mn> </mml:math> in the step-size and, third, a bound on the quadratic risk of the corresponding practical MCMC estimator (possibly with Richardson extrapolation). With respect to previous works on the Langevin diffusion, new difficulties arise from the jump operator, which is non-local.

SMAI Journal of Computational MathematicsVol. 11
Centre National de la Recherche Scientifique (FR), Institut Universitaire de France (FR), Université Paris Cité (FR), Sorbonne Université (FR), Laboratoire de Chimie Théorique (FR), University of Neuchâtel (CH)
National Science Foundation, Agence Nationale de la Recherche, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
Openalex Percentile: Top 61%
Advanced MRI Techniques and Applications
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The velocity jump Langevin process and its splitting scheme: long time convergence and numerical accuracy — Lucas Journel, Pierre Monmarché, et al. · SMAI Journal of Computational Mathematics (2025) | TGRS Research Map | TGRS