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Related papers: The Derivation and Approximation of Coarse-grained…

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As an example of the nonlinear Fokker-Planck equation, the mean field Langevin dynamics recently attracts attention due to its connection to (noisy) gradient descent on infinitely wide neural networks in the mean field regime, and hence the…

Machine Learning · Statistics 2022-02-28 Atsushi Nitanda , Denny Wu , Taiji Suzuki

We introduce a new method to accurately and efficiently estimate the effective dynamics of collective variables in molecular simulations. Such reduced dynamics play an essential role in the study of a broad class of processes, ranging from…

Statistical Mechanics · Physics 2022-03-28 Hadrien Vroylandt , Ludovic Goudenège , Pierre Monmarché , Fabio Pietrucci , Benjamin Rotenberg

We present a statistical mechanics framework for modeling equilibrium friction coefficients using the Generalized Langevin Equation (GLE). We show that the kernel, obtained via the Fluctuation-Dissipation Theorem (FDT) from the stochastic…

Plasma Physics · Physics 2026-05-01 N. R. Sree Harsha , Zhenyuan Yu , Chuang Ren , Virginia Billings , Michael Huang

A random multiplicative process with additive noise is described by a Langevin equation. We show that the fluctuation-dissipation relation is satisfied in the Langevin model, if the noise strength is not so strong.

Statistical Mechanics · Physics 2009-11-07 H. Sakaguchi

We prove quantitative convergence rates at which discrete Langevin-like processes converge to the invariant distribution of a related stochastic differential equation. We study the setup where the additive noise can be non-Gaussian and…

Machine Learning · Computer Science 2020-11-20 Xiang Cheng , Dong Yin , Peter L. Bartlett , Michael I. Jordan

This paper deals with the analysis of stochastic systems which can be described by a Langevin equation. By the method presented in this paper drift and diffusion terms of the corresponding Fokker-Planck equation can be extracted from the…

Condensed Matter · Physics 2009-10-31 S. Siegert , R. Friedrich , J. Peinke

Memory effects are a key feature in the description of the dynamical systems governed by the generalized Langevin equation, which presents an exact reformulation of the equation of motion. A simple measure for the estimation of memory…

Disordered Systems and Neural Networks · Physics 2007-05-23 Anatolii V. Mokshin , Renat M. Yulmetyev , Peter Hänggi

Memory effects emerge as a fundamental consequence of dimensionality reduction when low-dimensional observables are used to describe the dynamics of complex many-body systems. In the context of molecular dynamics (MD) data analysis,…

Data Analysis, Statistics and Probability · Physics 2024-05-01 Lucas Tepper , Benjamin Dalton , Roland R. Netz

We present a new method to approximate the Mori-Zwanzig (MZ) memory integral in generalized Langevin equations (GLEs) describing the evolution of smooth observables in high-dimensional nonlinear systems with local interactions. Building…

Numerical Analysis · Mathematics 2020-03-18 Yuanran Zhu , Daniele Venturi

Strong interaction with other particles or feedback from the medium on a Brownian particle entail memory effects in the effective dynamics. We discuss the extension of the fluctuation-dissipation theorem to nonequilibrium Langevin systems…

Statistical Mechanics · Physics 2013-09-23 C. Maes , S. Safaverdi , P. Visco , F. van Wijland

We derive the fluctuation dynamics of a probe in weak coupling with a "living" medium, modeled as particles undergoing an active Ornstein-Uhlenbeck dynamics. Nondissipative corrections to the fluctuation-dissipation relation are written out…

Soft Condensed Matter · Physics 2020-11-18 Christian Maes

Coarse-graining or model reduction is a term describing a range of approaches used to extend the time-scale of molecular simulations by reducing the number of degrees of freedom. In the context of molecular simulation, standard…

Dynamical Systems · Mathematics 2023-11-14 Thomas Hudson , Xingjie Helen Li

In molecular dynamics and sampling of high dimensional Gibbs measures coarse-graining is an important technique to reduce the dimensionality of the problem. We will study and quantify the coarse-graining error between the coarse-grained…

Analysis of PDEs · Mathematics 2020-06-04 M. H. Duong , A. Lamacz , M. A. Peletier , A. Schlichting , U. Sharma

We propose to describe the dynamics of phase transitions in terms of a non-stationary Generalized Langevin Equation for the order parameter. By construction, this equation is non-local in time, i.e.~it involves memory effects whose…

Statistical Mechanics · Physics 2021-02-10 Hugues Meyer , Fabian Glatzel , Wilkin Wöhler , Tanja SChilling

We introduce a constructive framework to learn effective Langevin equations from stationary time series. Unlike conventional approaches that require iterative calibration to match target statistics, our construction guarantees the observed…

Chaotic Dynamics · Physics 2026-02-16 Ludovico Theo Giorgini

We present a loss function for neural networks that encompasses an idea of trivial versus non-trivial predictions, such that the network jointly determines its own prediction goals and learns to satisfy them. This permits the network to…

Artificial Intelligence · Computer Science 2016-12-15 Nicholas Guttenberg , Martin Biehl , Ryota Kanai

We study large deviations in the Langevin dynamics, with damping of order $\e^{-1}$ and noise of order $1$, as $\e\downarrow 0$. The damping coefficient is assumed to be state dependent. We proceed first with a change of time and then, we…

Probability · Mathematics 2015-09-30 Sandra Cerrai , Mark Freidlin

Starting with a micropolar formulation, known to account for nonlocal microstructural effects at the continuum level, a generalized Langevin equation (GLE) for a particle, describing the predominant motion of a localized region through a…

Soft Condensed Matter · Physics 2015-09-30 Saikat Sarkar , Shubhankar Roy Chowdhury , Debasish Roy , Ram Mohan Vasu

We consider a classical model of non-equilibrium statistical mechanics accounting for non-Markovian effects, which is referred to as the Generalized Langevin Equation in the literature. We derive reduced Markovian descriptions obtained…

Statistical Mechanics · Physics 2024-05-28 Matteo Colangeli , Manh Hong Duong , Adrian Muntean

We propose a dynamical low-rank algorithm for a gyrokinetic model that is used to describe strongly magnetized plasmas. The low-rank approximation is based on a decomposition into variables parallel and perpendicular to the magnetic field,…

Computational Physics · Physics 2023-07-03 Lukas Einkemmer