Robust multi-stage model-based design of optimal experiments for nonlinear estimation
This work addresses robust experiment design for researchers in fields like chemical engineering or systems biology, offering incremental improvements over existing robustification frameworks.
The paper tackles the problem of designing optimal experiments for nonlinear parameter estimation under uncertainty, proposing a multi-stage robust optimization method that improves experiment sequencing and demonstrates effectiveness across four case studies.
We study approaches to robust model-based design of experiments in the context of maximum-likelihood estimation. These approaches provide robustification of model-based methodologies for the design of optimal experiments by accounting for the effect of the parametric uncertainty. We study the problem of robust optimal design of experiments in the framework of nonlinear least-squares parameter estimation using linearized confidence regions. We investigate several well-known robustification frameworks in this respect and propose a novel methodology based on multi-stage robust optimization. The proposed methodology aims at problems, where the experiments are designed sequentially with a possibility of re-estimation in-between the experiments. The multi-stage formalism aids in identifying experiments that are better conducted in the early phase of experimentation, where parameter knowledge is poor. We demonstrate the findings and effectiveness of the proposed methodology using four case studies of varying complexity.