Computing Large Deviation Rate Functions of Entropy Production for Diffusion Processes by an Interacting Particle Method

We develop an interacting particle method (IPM) for computing the large deviation rate function of entropy production for diffusion processes, with emphasis on the vanishing-noise limit and high dimensions. The crucial ingredient for obtaining the rate function is the computation of the principal eigenvalue 𝜆 of elliptic, non-self-adjoint operators. We show that this principal eigenvalue can be approximated in terms of the spectral radius of a discretized evolution operator, which is obtained from an operator splitting scheme and an Euler–Maruyama scheme with a small time step size. We also show that this spectral radius can be accessed through a large number of iterations of this discretized semigroup, which is suitable for computation using the IPM. The IPM applies naturally to problems in unbounded domains and scales easily to high dimensions. We show numerical examples of dimensions up to 16, and the results show that our numerical approximation of 𝜆 converges to the analytical vanishing-noise limit within visual tolerance with a fixed number of particles and a fixed time step size. It is numerically shown that the IPM can adapt to singular behaviors in the vanishing-noise limit. We also apply the IPM to explore situations with no explicit formulas of the vanishing-noise limit. Our paper appears to be the first to obtain numerical results of principal eigenvalue problems for non-self-adjoint operators in such high dimensions.<br>

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Publication Details

Journal
SIAM Journal on Scientific Computing
Published
2025-11-05
DOI
https://doi.org/10.1137/24m1720172
Primary Topic
Diffusion and Search Dynamics
Type
article
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Computing Large Deviation Rate Functions of Entropy Production for Diffusion Processes by an Interacting Particle Method

Jack Xin, Zhizhang Wu, Renaud Raquépas, Zhiwen Zhang
SIAM Journal on Scientific Computing
Diffusion and Search Dynamics
article

Computing Large Deviation Rate Functions of Entropy Production for Diffusion Processes by an Interacting Particle Method

Jack Xin, Zhizhang Wu, Renaud Raquépas, Zhiwen Zhang
article en

Abstract

We develop an interacting particle method (IPM) for computing the large deviation rate function of entropy production for diffusion processes, with emphasis on the vanishing-noise limit and high dimensions. The crucial ingredient for obtaining the rate function is the computation of the principal eigenvalue 𝜆 of elliptic, non-self-adjoint operators. We show that this principal eigenvalue can be approximated in terms of the spectral radius of a discretized evolution operator, which is obtained from an operator splitting scheme and an Euler–Maruyama scheme with a small time step size. We also show that this spectral radius can be accessed through a large number of iterations of this discretized semigroup, which is suitable for computation using the IPM. The IPM applies naturally to problems in unbounded domains and scales easily to high dimensions. We show numerical examples of dimensions up to 16, and the results show that our numerical approximation of 𝜆 converges to the analytical vanishing-noise limit within visual tolerance with a fixed number of particles and a fixed time step size. It is numerically shown that the IPM can adapt to singular behaviors in the vanishing-noise limit. We also apply the IPM to explore situations with no explicit formulas of the vanishing-noise limit. Our paper appears to be the first to obtain numerical results of principal eigenvalue problems for non-self-adjoint operators in such high dimensions.<br>

SIAM Journal on Scientific ComputingVol. 47(6)
University of California, Irvine (US), Courant Institute of Mathematical Sciences (US), Institut de Recherche et d’Innovation (FR), University of Hong Kong (HK)
Openalex Percentile: Top 71%
Diffusion and Search Dynamics
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Computing Large Deviation Rate Functions of Entropy Production for Diffusion Processes by an Interacting Particle Method — Jack Xin, Zhizhang Wu, et al. · SIAM Journal on Scientific Computing (2025) | TGRS Research Map | TGRS