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arXiv · 2108.01590

Temperature Steerable Flows and Boltzmann Generators

Manuel Dibak·Leon Klein·Andreas Krämer·Frank Noé
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Abstract

Boltzmann generators approach the sampling problem in many-body physics by combining a normalizing flow and a statistical reweighting method to generate samples in thermodynamic equilibrium. The equilibrium distribution is usually defined by an energy function and a thermodynamic state. Here we propose temperature-steerable flows (TSF) which are able to generate a family of probability densities parametrized by a choosable temperature parameter. TSFs can be embedded in generalized ensemble sampling frameworks to sample a physical system across multiple thermodynamic states.

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Manuel Dibak, Leon Klein, Andreas Krämer, Frank Noé. 2021-08-03. Temperature Steerable Flows and Boltzmann Generators. https://arxiv.org/abs/2108.01590

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