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Related papers: Onsager-Machlup functional for stochastic differen…

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In this paper, we derive the Onsager-Machlup functional for stochastic differential equations driven by time-varying fractional noise of the form X(t) = x0 + integral from 0 to t b_s(X(s)) ds + integral from 0 to t sigma_s dB^H(s), where…

Probability · Mathematics 2025-11-13 Yanbin Zhu , Xiaomeng Jiang , Yong Li

This paper investigates the Onsager-Machlup functional of stochastic lattice dynamical systems (SLDSs) driven by time-varying noise. We extend the Onsager-Machlup functional from finite-dimensional to infinite-dimensional systems, and from…

Probability · Mathematics 2024-08-19 Xinze Zhang , Yong Li

This work is devoted to deriving the Onsager-Machlup function for a class of stochastic dynamical systems under (non-Gaussian) Levy noise as well as (Gaussian) Brownian noise, and examining the corresponding most probable paths. This…

Mathematical Physics · Physics 2020-01-08 Ying Chao , Jinqiao Duan

This work is devoted to deriving the Onsager--Machlup function for a class of degenerate stochastic dynamical systems with (non-Gaussian) L\'{e}vy noise as well as Brownian noise. This is obtained based on the Girsanov transformation and…

Dynamical Systems · Mathematics 2025-01-10 Ying Chao , Pingyuan Wei

In this paper, we derive the Onsager--Machlup functional for a second-order Newton-type stochastic system driven by time-dependent fractional noise, \[ X_t'' = f_t(X_t, X_t') + \sigma_t \,\xi_t^{H}, \] where \( H \in (1/4,1) \). The…

Dynamical Systems · Mathematics 2026-02-25 Yanbin Zhu , Xiaomeng Jiang , Yong Li

This work is devoted to deriving the Onsager-Machlup action functional for stochastic partial differential equations with (non-Gaussian) Levy process as well as Gaussian Brownian motion. This is achieved by applying the Girsanov…

Probability · Mathematics 2020-12-07 Jianyu Hu , Jinqiao Duan

This paper investigates bifurcation phenomena and stability of most probable transition paths (MPTPs) in stochastic dynamical systems through a combined variational and spectral flow approach. Within the Onsager-Machlup framework, MPTPs are…

Dynamical Systems · Mathematics 2025-08-13 Jinqiao Duan , Zhihao Zhao

This work is devoted to deriving the Onsager-Machlup action functional for a class of stochastic differential equations with (non-Gaussian) L\'{e}vy process as well as Brownian motion in high dimensions. This is achieved by applying the…

Dynamical Systems · Mathematics 2024-06-19 Jianyu Hu , Jianyu Chen

We consider the solution of a stochastic differential equation with additive multidimensional fractional noise. In the case $\frac14<H<\frac12$, we compute the Onsager-Machlup functional (with respect to the driving fractional Brownian…

Probability · Mathematics 2017-05-26 Yohaï Maayan

The Onsager--Machlup action functional is an important concept in statistical mechanics and thermodynamics to describe the probability of fluctuations in nonequilibrium systems. It provides a powerful tool for analyzing and predicting the…

Probability · Mathematics 2024-12-03 Yuanfei Huang , Xiang Zhou , Jinqiao Duan

The definition and manipulation of Langevin equations with multiplicative white noise require special care (one has to specify the time discretisation and a stochastic chain rule has to be used to perform changes of variables). While…

Statistical Mechanics · Physics 2022-08-31 Leticia F. Cugliandolo , Vivien Lecomte

The purpose of this paper is to investigate the existence of the Onsager-Machlup action functional for degenerate McKean-Vlasov stochastic differential equations. To this end, we first derive Onsager-Machlup action functional for degenerate…

Probability · Mathematics 2023-12-07 Shanqi Liu , Hongjun Gao

The Onsager principle provides a variational route to the phenomenological equations of dissipative dynamics through the minimization of the Rayleighian. We develop a covariant formulation of the Onsager principle for active systems,…

The emergence of transition phenomena between metastable states induced by noise plays a fundamental role in a broad range of nonlinear systems. The computation of the most probable paths is a key issue to understand the mechanism of…

Dynamical Systems · Mathematics 2021-01-27 Yang Li , Jinqiao Duan , Xianbin Liu

In this paper, the explicit expression of Onsager-Machlup action functional to degenerate stochastic differential equations driven by fractional Brownian motion is derived provided the diffusion coeffcient and reference path satisfy some…

Probability · Mathematics 2023-12-07 Shanqi Liu , Hongjun Gao

Extracting governing stochastic differential equation models from elusive data is crucial to understand and forecast dynamics for complex systems. We devise a method to extract the drift term and estimate the diffusion coefficient of a…

Numerical Analysis · Mathematics 2020-08-21 Jian Ren , Jinqiao Duan

The concept of stochastic Lagrangian and its use in statistical dynamics is illustrated theoretically, and with some examples. Dynamical variables undergoing stochastic differential equations are stochastic processes themselves, and their…

Statistical Mechanics · Physics 2020-03-18 Massimo Materassi

This work is devoted to the investigation of the most probable transition time between metastable states for stochastic dynamical systems. Such a system is modeled by a stochastic differential equation with non-vanishing Brownian noise, and…

Mathematical Physics · Physics 2021-08-11 Yuanfei Huang , Ying Chao , Wei Wei , Jinqiao Duan

A high-accuracy time discretization is discussed to numerically solve the nonlinear fractional diffusion equation forced by a space-time white noise. The main purpose of this paper is to improve the temporal convergence rate by modifying…

Numerical Analysis · Mathematics 2021-05-04 Xing Liu

We study Langevin dynamics with stochastic diffusivity arising from fluctuations of the surrounding medium. The diffusivity is modeled as Ornstein-Uhlenbeck process driven by symmetric dichotomous noise, which confines it to a finite…

Statistical Mechanics · Physics 2026-04-14 Dongho Lee , Jae-Hyung Jeon , Pascal Viot , Gleb Oshanin
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