The optimal experiment? Influence of solution strategies on model-based optimal experimental design

Abstract

Model-based optimal experimental design (OED) is a well known tool for efficient model development. However, it is not used very often. A few reasons for that are: a lack of understanding on how to work with complex OED methods and a small amount of ready-to-use tools available to directly apply OED methods. In the presented contribution OED and sampling strategies are used to categorize OED formulations as nonlinear programs. Different strategies and their combination are analyzed based on performance and robustness. Depending on the availability of measurements, control flexibility of the experimental setup, and model accuracy some strategies are more efficient than others. Based on the proposed guidelines, engineers will have a better understanding about which NLP formulation should be used for their specific task. The methods described are available to the community as a part of open-source code developed in Python.