论文标题
温度驱动流动
Temperature-steerable flows
论文作者
论文摘要
Boltzmann发电机通过结合标准化流量和统计重新加权方法来生成物理系统平衡密度的样品，从而解决多体物理的采样问题。平衡分布通常由能量功能和热力学状态（例如给定温度）定义。在这里，我们提出了可温度转动的流（TSF），该流量能够生成由可选温度参数参数参数的概率密度的家族。 TSF可以嵌入广义的集合采样框架中，例如平行回火，以便在热力学状态（例如多种温度）跨性别系统中采样物理系统。
Boltzmann generators approach the sampling problem in many-body physics by combining a normalizing flow and a statistical reweighting method to generate samples of a physical system's equilibrium density. The equilibrium distribution is usually defined by an energy function and a thermodynamic state, such as a given temperature. 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 a generalized ensemble sampling framework such as parallel tempering in order to sample a physical system across thermodynamic states, such as multiple temperatures.
