Optimal Experimental Design for Uncertain Systems Based on Coupled Differential Equations

Abstract

We consider the optimal experimental design problem for an uncertain Kuramoto model, which consists of N interacting oscillators described by coupled ordinary differential equations. The objective is to design experiments that can effectively reduce the uncertainty present in the coupling strengths between the oscillators, thereby minimizing the cost of robust control of the uncertain Kuramoto model. We demonstrate the importance of quantifying the operational impact of the potential experiments in designing optimal experiments.

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• Robust Importance Sampling for Error Estimation in the Context of Optimal Bayesian Transfer Learning
• Neural Message Passing for Objective-Based Uncertainty Quantification and Optimal Experimental Design