Accelerating Optimal Experimental Design for Robust Synchronization of Uncertain Kuramoto Oscillator Model Using Machine Learning
Accelerating Optimal Experimental Design for Robust Synchronization of Uncertain Kuramoto Oscillator Model Using Machine Learning
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DOI：
10.1109/tsp.2021.3130967
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发表时间：
2021-01-01
影响因子：
5.4
通讯作者：
Yoon, Byung-Jun
中科院分区：
文献类型：
--
作者：
Woo, Hyun-Myung;Hong, Youngjoon;Yoon, Byung-Jun
基于目标的不确定性量化（objective-UQ）的最新进展表明，这种目标驱动的量化模型不确定性的方法在现实世界中的问题中非常有用，这些问题旨在实现基于复杂不确定系统的特定目标。这一目标的核心是不确定性的平均客观成本概念，它提供了量化不确定性对当前业务目标影响的有效手段。MOCU对于最优实验设计（OED）特别有用，因为实验（或数据采集）活动的潜在功效可以通过估计活动后预计保留的MOCU来量化。然而，基于MOCU的OED往往是计算昂贵的，这限制了它的实用性。在本文中，我们提出了一种新的机器学习（ML）方案，可以显着加快MOCU计算和加快基于MOCU的实验设计。其主要思想是使用ML模型来有效地搜索模型不确定性下的最优鲁棒算子，这是计算MOCU的必要步骤。我们应用建议ML为基础的OED加速计划设计实验，旨在最佳地提高不确定的仓本振荡器模型的控制性能。我们的研究结果表明，该方案的结果在高达154倍的速度提高，而没有任何退化的OED性能。
Recent advances in objective-based uncertainty-quantification (objective-UQ) have shown that such a goal-driven approach for quantifying model uncertainty is extremely usefulin 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.