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arXiv · 2406.09557

Measure This, Not That: Optimizing the Cost and Model-Based Information Content of Measurements

Jialu Wang·Zedong Peng·Ryan Hughes·Debangsu Bhattacharyya·David E. Bernal Neira·Alexander W. Dowling
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Abstract

Model-based design of experiments (MBDoE) is a powerful framework for selecting and calibrating science-based mathematical models from data. This work extends popular MBDoE workflows by proposing a convex mixed integer (non)linear programming (MINLP) problem to optimize the selection of measurements. The solver MindtPy is modified to support calculating the D-optimality objective and its gradient via an external package, \texttt{SciPy}, using the grey-box module in Pyomo. The new approach is demonstrated in two case studies: estimating highly correlated kinetics from a batch reactor and estimating transport parameters in a large-scale rotary packed bed for CO$_2$ capture. Both case studies show how examining the Pareto-optimal trade-offs between information content measured by A- and D-optimality versus measurement budget offers practical guidance for selecting measurements for scientific experiments.

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Jialu Wang, Zedong Peng, Ryan Hughes, Debangsu Bhattacharyya, David E. Bernal Neira, Alexander W. Dowling. 2024-06-13. Measure This, Not That: Optimizing the Cost and Model-Based Information Content of Measurements. https://arxiv.org/abs/2406.09557

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