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Autori principali: Liu, Chuyi, Guan, Yifeng, Li, Jingyuan, Su, Mao
Natura: Preprint
Pubblicazione: 2026
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Accesso online:https://arxiv.org/abs/2604.16825
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author Liu, Chuyi
Guan, Yifeng
Li, Jingyuan
Su, Mao
author_facet Liu, Chuyi
Guan, Yifeng
Li, Jingyuan
Su, Mao
contents We introduce the spatial disorder-generalized Langevin equation (SD-GLE), a data-driven method for constructing coarse-grained (CG) dynamics in heterogeneous systems. Unlike conventional CG approaches that rely on a mean-field potential, SD-GLE utilizes a variational Bayesian framework with a random field prior to explicitly disentangle static spatial disorder from viscoelastic friction. Numerical results demonstrate the limits of standard GLEs, whereas SD-GLE accurately extrapolates long-time dynamics to capture the anomalous diffusion crossover from short trajectories and recover the ensemble statistical properties inherent to the disordered nature of these systems.
format Preprint
id arxiv_https___arxiv_org_abs_2604_16825
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Coarse-Grained Dynamics with Spatial Disorder and Non-Markovian Memory
Liu, Chuyi
Guan, Yifeng
Li, Jingyuan
Su, Mao
Computational Physics
We introduce the spatial disorder-generalized Langevin equation (SD-GLE), a data-driven method for constructing coarse-grained (CG) dynamics in heterogeneous systems. Unlike conventional CG approaches that rely on a mean-field potential, SD-GLE utilizes a variational Bayesian framework with a random field prior to explicitly disentangle static spatial disorder from viscoelastic friction. Numerical results demonstrate the limits of standard GLEs, whereas SD-GLE accurately extrapolates long-time dynamics to capture the anomalous diffusion crossover from short trajectories and recover the ensemble statistical properties inherent to the disordered nature of these systems.
title Coarse-Grained Dynamics with Spatial Disorder and Non-Markovian Memory
topic Computational Physics
url https://arxiv.org/abs/2604.16825