Simulation models of critical systems often have parameters that need to be calibrated using observed data. For expensive simulation models, calibration is done using an emulator of the simulation model built on simulation output at different parameter settings. Using intelligent and adaptive selection of parameters to build the emulator can drastically improve the efficiency of the calibration process. The article proposes a sequential framework with a novel criterion for parameter selection that targets learning the posterior density of the parameters. The emergent behavior from this criterion is that exploration happens by selecting parameters in uncertain posterior regions while simultaneously exploitation happens by selecting parameters in regions of high posterior density. The advantages of the proposed method are illustrated using several simulation experiments and a nuclear physics reaction model.

翻译：关键系统的仿真模型通常包含需要使用观测数据进行校准的参数。对于昂贵的仿真模型，校准是通过基于不同参数设置下的仿真输出所构建的仿真模型代理器来完成的。采用智能且自适应的参数选择方法构建代理器可显著提升校准过程的效率。本文提出了一种基于新颖参数选择准则的顺序框架，其目标是学习参数的后验密度。该准则的涌现行为表现为：一方面通过选择后验不确定区域中的参数进行探索，另一方面通过选择后验高密度区域的参数进行开发。通过多项仿真实验及一个核物理反应模型验证了所提出方法的优势。