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| Autori principali: | , , , |
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| Natura: | Preprint |
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2026
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2604.16825 |
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| _version_ | 1866917417852076032 |
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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 |