Generalized Computer Model Calibration for Radiation Transport Simulation

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.

模型校准（model calibration）通过模拟器输出与现场实测数据，为物理系统构建预测模型并估算未知输入项。传统模型校准方法假设观测值为连续型结果，但在诸多实际应用场景中，该假设并不成立。本文提出的方法源自千万亿次辐射输运中心（Center for Exascale Radiation Transport）的光子计数建模应用，该中心依托高性能计算模拟中子在多种材料中的输运过程。本文针对观测数据的计数结构，提出了适用于计算机模型校准的贝叶斯方法（Bayesian methodology），该方法能够更贴近真实实验系统，且可灵活识别模拟器与真实实验间的多种形式的模型偏差。本文的补充材料可在线获取。