Stochastic simulators are an indispensable tool in many branches of science. Often based on first principles, they deliver a series of samples whose distribution implicitly defines a probability measure to describe the phenomena of interest. However, the fidelity of these simulators is not always sufficient for all scientific purposes, necessitating the construction of ad-hoc corrections to "calibrate" the simulation and ensure that its output is a faithful representation of reality. In this paper, we leverage methods from transportation theory to construct such corrections in a systematic way. We use a neural network to compute minimal modifications to the individual samples produced by the simulator such that the resulting distribution becomes properly calibrated. We illustrate the method and its benefits in the context of experimental particle physics, where the need for calibrated stochastic simulators is particularly pronounced.

翻译：视觉模拟器是许多科学分支中不可或缺的工具。 通常基于第一原则, 它们提供一系列样本, 其分布暗含地界定了描述感兴趣的现象的概率度量。 然而, 这些模拟器的忠实性并不总是对所有科学目的都足够, 因此有必要构建“ 校正” 模拟的特设校正, 并确保其输出真实地反映现实。 在本文中, 我们利用运输理论的方法来系统构建这样的校正。 我们使用神经网络来计算模拟器所生成的单个样本的微小修改, 从而对结果的分布进行适当校准。 我们从实验性粒子物理学的角度来说明方法及其好处, 因为在实验性粒子物理学中, 校准的随机模拟器的需求特别明显 。