Optimal Experiment Campaigns under Uncertainty Minimizing Bayes Risk

Applying model-based design of experiments to compute maximally-informative campaigns with multiple parallel runs is challenging. Herein, we develop a systematic framework for recasting an experiment design problem for model parameter precision as one of discrimination between multiple rival models with different uncertain parameter realizations. We use an algebraic upper bound on the Bayes Risk as information criterion and apply a search procedure that iterates between an effort-based optimization step followed by a gradient-based refinement step. Through the case study of a fed-batch reactor, we show that a Bayes Risk discrimination strategy can provide highly-informative experimental campaigns to improve parameter precision, while being computationally advantageous compared to conventional FIM-based design strategies and capable of handling structurally unidentifiable problems.