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Such coarse-graining procedures usually need substantial experience and/or a-deep understanding of the device characteristics. In this paper we present a systematic, data-driven approach to discovering "bespoke" coarse factors based on manifold learning formulas. We illustrate this methodology aided by the classic Kuramoto period oscillator design, and show how our manifold learning strategy can successfully determine a coarse adjustable this is certainly one-to-one ut
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