Engineers are often faced with the decision to select the most appropriate model for simulating the behavior of engineered systems, among a candidate set of models. Experimental monitoring data can generate significant value by supporting engineers toward such decisions. Such data can be leveraged within a Bayesian model updating process, enabling the uncertainty-aware calibration of any candidate model. The model selection task can subsequently be cast into a problem of decision-making under uncertainty, where one seeks to select the model that yields an optimal balance between the reward associated with model precision, in terms of recovering target Quantities of Interest (QoI), and the cost of each model, in terms of complexity and compute time. In this work, we examine the model selection task by means of Bayesian decision theory, under the prism of availability of models of various refinements, and thus varying levels of fidelity. In doing so, we offer an exemplary application of this framework on the IMAC-MVUQ Round-Robin Challenge. Numerical investigations show various outcomes of model selection depending on the target QoI.

翻译：工程师常需从候选模型集合中选出最能模拟工程系统行为的模型。实验监测数据通过支持工程师做出此类决策可产生重要价值。此类数据可整合至贝叶斯模型更新过程中，实现候选模型的不确定性感知校准。模型选择任务随后可转化为不确定性条件下的决策问题：需在模型精度（关于目标关注量（QoI）的恢复）对应的收益与模型复杂度及计算时间对应的成本之间寻求最优平衡。本文基于贝叶斯决策理论，从不同精细度模型（即不同保真度模型）的可用性视角审视模型选择任务，并在IMAC-MVUQ轮询挑战赛上展示了该框架的典型应用案例。数值研究表明，模型选择结果因目标QoI不同而呈现多样性。