Aggregation Methods for Computing Steady-States in Statistical Physics

Gabriel Earle; Brian Van Koten

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arXiv · 2209.11164

Aggregation Methods for Computing Steady-States in Statistical Physics

Gabriel Earle·Brian Van Koten

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Abstract

We give a new proof of local convergence of a multigrid method called iterative aggregation/disaggregation (IAD) for computing steady-states of Markov chains. Our proof leads naturally to a precise and interpretable estimate of the asymptotic rate of convergence. We study IAD as a model of more complex methods from statistical physics for computing nonequilibrium steady-states, such as the nonequilibrium umbrella sampling method of Warmflash, et al. We explain why it may be possible to use methods like IAD to efficiently calculate steady-states of models in statistical physics and how to choose parameters to optimize efficiency.

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math.NA cond-mat.stat-mech cs.NA physics.chem-ph

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Gabriel Earle, Brian Van Koten. 2022-09-22. Aggregation Methods for Computing Steady-States in Statistical Physics. https://arxiv.org/abs/2209.11164

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