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arXiv 2511.05366cond-mat.stat-mechmath.DSphysics.bio-ph
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基于马尔可夫链的非平衡扩散粗粒化

Coarse-graining nonequilibrium diffusions with Markov chains

Mathematical Institute, University of Oxford(数学研究所,牛津大学);Centre for Eudaimonia and Human Flourishing, University of Oxford(幸福与人类繁荣中心,牛津大学)

Ramón Nartallo-Kaluarachchi, Renaud Lambiotte, Alain Goriely

AI总结:

本文研究了连续和离散状态随机过程的非平衡稳态动力学,通过离散状态马尔可夫链对平面扩散进行粗粒化近似,展示了如何通过有限体积近似推导主方程,并验证了离散状态对非平衡稳态特征的保留性。

摘要:

我们研究了连续和离散状态随机过程中的非平衡稳态动力学。我们的分析重点在于平面扩散动力学及其通过离散状态马尔可夫链进行的粗粒化近似。利用有限体积近似,我们直接从底层扩散推导出近似的主方程,并证明这种离散化保留了非平衡稳态的关键特征。特别是,我们证明当离散状态数量趋于无穷时,近似的熵产率收敛。这些结果通过可解析求解的扩散和非线性过程的数值实验进行说明,展示了该方法如何用于探索熵产率对模型参数的依赖性。最后,我们处理了从连续随机轨迹推断离散状态马尔可夫模型的问题。我们证明离散状态模型显著低估了真实的熵产率。然而,我们还证明它们可以用于测试是否处于非平衡状态。这一性质通过模拟数据和鱼群行为的实证轨迹进行了说明。

英文摘要:

We investigate nonequilibrium steady-state dynamics in both continuous- and discrete-state stochastic processes. Our analysis focuses on planar diffusion dynamics and their coarse-grained approximations by discrete-state Markov chains. Using finite-volume approximations, we derive an approximate master equation directly from the underlying diffusion and show that this discretisation preserves key features of the nonequilibrium steady-state. In particular, we show that the entropy production rate of the approximation converges as the number of discrete states goes to the limit. These results are illustrated with analytically solvable diffusions and numerical experiments on nonlinear processes, demonstrating how this approach can be used to explore the dependence of the entropy production rate on model parameters. Finally, we address the problem of inferring discrete-state Markov models from continuous stochastic trajectories. We show that discrete-state models significantly underestimate the true entropy production rate. However, we also show that they can provide tests to determine if a stationary planar diffusion is out of equilibrium. This property is illustrated with both simulated data and empirical trajectories from schooling fish.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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