Phase-isostable reduction is an emerging model reduction strategy that can be used to accurately replicate nonlinear behaviors in systems for which standard phase reduction techniques fail. In this work, we derive relationships between the cycle-to-cycle variance of the reduced isostable coordinates for systems subject to both additive white noise and periodic stimulation. Using this information, we propose a data-driven technique for inferring nonlinear terms of the phase-isostable coordinate reduction framework. We apply the proposed model inference strategy to the biologically motivated problem of eliminating cardiac alternans, an arrhythmia that is widely considered to be a precursor to more deadly cardiac arrhythmias. Using this strategy, by simply measuring a series of action potential durations in response to periodic stimulation, we are able to identify energy-optimal, nonfeedback control inputs to stabilize a period-1, alternans-free solution.

中文翻译：

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数据驱动的相平衡减少，可实现心脏交替神经的最佳非反馈稳定

相位稳定减少是一种新兴的模型减少策略，可用于精确复制标准相位减少技术失败的系统中的非线性行为。在这项工作中，我们得出了受加性白噪声和周期性刺激共同作用的系统的降低的可耗减坐标的周期之间的方差之间的关系。利用这些信息，我们提出了一种数据驱动技术，用于推断相稳定坐标降低框架的非线性项。我们将提出的模型推论策略应用于消除心脏交替信号的生物学动机问题，心脏交替信号被广泛认为是致命性心律不齐的先兆。使用这种策略，只需简单地测量响应周期性刺激的一系列动作电位持续时间，