A new class of splitting methods that preserve ergodicity and exponential integrability for stochastic Langevin equation

Détail du document

Identifiant

oai:arXiv.org:2410.20938

Sujet

Mathematics - Numerical Analysis Mathematics - Probability

Auteur

Chen, Chuchu Dang, Tonghe Hong, Jialin Zhang, Fengshan

Catégorie

Computer Science

Année

2024

Date de référencement

30/10/2024

Mots clés

preserve stochastic equation ergodicity methods

Métrique

Résumé

In this paper, we propose a new class of splitting methods to solve the stochastic Langevin equation, which can simultaneously preserve the ergodicity and exponential integrability of the original equation.

The central idea is to extract a stochastic subsystem that possesses the strict dissipation from the original equation, which is inspired by the inheritance of the Lyapunov structure for obtaining the ergodicity.

We prove that the exponential moment of the numerical solution is bounded, thus validating the exponential integrability of the proposed methods.

Further, we show that under moderate verifiable conditions, the methods have the first-order convergence in both strong and weak senses, and we present several concrete splitting schemes based on the methods.

The splitting strategy of methods can be readily extended to construct conformal symplectic methods and high-order methods that preserve both the ergodicity and the exponential integrability, as demonstrated in numerical experiments.

Our numerical experiments also show that the proposed methods have good performance in the long-time simulation.

Chen, Chuchu,Dang, Tonghe,Hong, Jialin,Zhang, Fengshan, 2024, A new class of splitting methods that preserve ergodicity and exponential integrability for stochastic Langevin equation

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