Bayesian Decision-Theoretic Model Selection for Monitored Systems
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Date
2025
Publication Type
Conference Paper
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yes
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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.
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Publication status
published
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Book title
Model Validation and Uncertainty Quantification, Vol. 3
Volume
Pages / Article No.
115 - 121
Publisher
Springer
Event
42nd IMAC, A Conference and Exposition on Structural Dynamics (IMAC 2024)
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Subject
Model selection; Decision theory; Bayesian inference; Model updating; Value of information; Monitoring
Organisational unit
03890 - Chatzi, Eleni / Chatzi, Eleni