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）借助模拟器输出与现场实测数据，构建物理系统的预测模型并估计未知输入参数。传统模型校准方法默认观测结果为连续型变量，但在诸多实际应用中该假设并不成立。本文提出的方法源自百亿亿次辐射传输中心（Center for Exascale Radiation Transport）的光子计数（Photon Counts）建模应用场景：该中心依托高性能计算模拟中子在各类材料中的输运过程。针对观测数据的计数型结构，本文提出全新的贝叶斯计算机模型校准（Computer Model Calibration）方法，可实现与实验系统更高的保真度，并能灵活识别模拟器与真实实验间不同形式的模型偏差（Model Discrepancy）。本文补充材料可在线获取。