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

Source: [Measure This, Not That: Optimizing the Cost and Model-Based Information Content of Measurements](<https://www.sota2.com/research/paper/68fa5bec1e209131ce9ccaf1>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-27T05:33:22.966Z

Authors: Jialu Wang, Zedong Peng, Ryan Hughes, Debangsu Bhattacharyya, David E. Bernal Neira, Alexander W. Dowling

Published: 2024-06-13T19:43:47.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2406.09557>)

[PDF](<https://arxiv.org/pdf/2406.09557.pdf>)