Measure This, Not That: Optimizing the Cost and Model-Based Information Content of Measurements
<|reference_start|>Measure This, Not That: Optimizing the Cost and Model-Based Information Content of Measurements: 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.<|reference_end|>
@article{wang2024measure,
    title={Measure This, Not That: Optimizing the Cost and Model-Based Information
  Content of Measurements},
    author={Jialu Wang, Zedong Peng, Ryan Hughes, Debangsu Bhattacharyya, David E.
  Bernal Neira, Alexander W. Dowling},
    journal={arXiv preprint arXiv:2406.09557},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.09557},
    primaryClass={math.OC cs.SY eess.SY stat.AP}
}