Abstract Model calibration uses outputs from a simulator and field data to build a predictive model for the physical system and to estimate unknown inputs. The conventional approach to model calibration assumes that the observations are continuous outcomes. In many applications this is not the case. The methodology proposed was motivated by an application in modeling photon counts at the Center for Exascale Radiation Transport. There, high performance computing is used for simulating the flow of neutrons through various materials. In this article, new Bayesian methodology for computer model calibration to handle the count structure of our observed data allows closer fidelity to the experimental system and provides flexibility for identifying different forms of model discrepancy between the simulator and experiment. Supplementary materials for this article are available online.

中文翻译：

---

辐射传输模拟的广义计算机模型校准

摘要 模型校准使用来自模拟器的输出和现场数据来构建物理系统的预测模型并估计未知输入。模型校准的传统方法假设观察是连续的结果。在许多应用中，情况并非如此。提议的方法是由在百亿亿级辐射传输中心模拟光子计数的应用程序激发的。在那里，高性能计算用于模拟中子通过各种材料的流动。在本文中，用于计算机模型校准的新贝叶斯方法来处理我们观察到的数据的计数结构，可以更接近于实验系统，并为识别模拟器和实验之间不同形式的模型差异提供了灵活性。