EOMC shows that chaotic systems like Lorenz-96 and tokamak turbulence are best captured as metastable switches between persistent low-dimensional manifolds with slowly decreasing exit times.
Journal of Machine Learning Research (JMLR)9, 2579–2605 (2008)
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Linearly-scalable and entropy-optimal learning of nonstationary and nonlinear manifolds
EOMC shows that chaotic systems like Lorenz-96 and tokamak turbulence are best captured as metastable switches between persistent low-dimensional manifolds with slowly decreasing exit times.