Accelerating Optimal Experimental Design for Robust Synchronization of Uncertain Kuramoto Oscillator Model Using Machine Learning

TL;DR

This paper introduces a machine learning approach to significantly speed up the computation of MOCU for optimal experimental design, enabling efficient synchronization control of uncertain Kuramoto oscillators with minimal performance loss.

Contribution

A novel ML-based method is developed to accelerate MOCU computation, improving the practicality of optimal experimental design for uncertain oscillator models.

Findings

Achieves up to 154-fold speedup in MOCU computation

Maintains OED performance despite acceleration

Effectively enhances control of uncertain Kuramoto oscillators

Abstract

Recent advances in objective-based uncertainty quantification (objective-UQ) have shown that such a goal-driven approach for quantifying model uncertainty is extremely useful in real-world problems that aim at achieving specific objectives based on complex uncertain systems. Central to this objective-UQ is the concept of mean objective cost of uncertainty (MOCU), which provides effective means of quantifying the impact of uncertainty on the operational goals at hand. MOCU is especially useful for optimal experimental design (OED) as the potential efficacy of an experimental (or data acquisition) campaign can be quantified by estimating the MOCU that is expected to remain after the campaign. However, MOCU-based OED tends to be computationally expensive, which limits its practical applicability. In this paper, we propose a novel machine learning (ML) scheme that can significantly accelerate MOCU computation and expedite MOCU-based experimental design. The main idea is to use an ML model to efficiently search for the optimal robust operator under model uncertainty, a necessary step for computing MOCU. We apply the proposed ML-based OED acceleration scheme to design experiments aimed at optimally enhancing the control performance of uncertain Kuramoto oscillator models. Our results show that the proposed scheme results in up to 154-fold speed improvement without any degradation of the OED performance.