Title: Bayesian decision-theoretic model selection for monitored systems Show Chinese title

Title: 基于贝叶斯决策理论的监控系统模型选择

Abstract: 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. Show Chinese abstract

Abstract: 工程师经常需要在一组候选模型中选择最合适的模型来模拟工程系统的行为。 实验监测数据可以通过帮助工程师做出此类决策来产生显著的价值。 这些数据可以在贝叶斯模型更新过程中加以利用，从而实现任何候选模型的不确定性感知校准。 随后，模型选择任务可以转化为一种在不确定性下的决策问题，其中寻求选择一个在模型精度所带来的奖励（以恢复目标感兴趣的量QoI）和每个模型的成本（以复杂性和计算时间表示）之间取得最佳平衡的模型。 在本工作中，我们通过贝叶斯决策理论来研究模型选择任务，在不同细化程度的模型可用性的视角下，因此是不同层次的保真度。 在此过程中，我们在IMAC-MVUQ Round-Robin挑战赛中提供了一个该框架的示例应用。 数值研究显示，模型选择的各种结果取决于目标QoI。