Modeling of Missing Dynamical Systems: Deriving Parametric Models using a Nonparametric Framework

Abstract

In this paper, we consider modeling missing dynamics with a nonparametric non-Markovian model, constructed using the theory of kernel embedding of conditional distributions on appropriate Reproducing Kernel Hilbert Spaces (RKHS), equipped with orthonormal basis functions. Depending on the choice of the basis functions, the resulting closure model from this nonparametric modeling formulation is in the form of parametric model. This suggests that the success of various parametric modeling approaches that were proposed in various domains of applications can be understood through the RKHS representations. When the missing dynamical terms evolve faster than the relevant observable of interest, the proposed approach is consistent with the effective dynamics derived from the classical averaging theory. In the linear Gaussian case without the time-scale gap, we will show that the proposed non-Markovian model with a very long memory yields an accurate estimation of the nontrivial autocovariance function for the relevant variable of the full dynamics. Supporting numerical results on instructive nonlinear dynamics show that the proposed approach is able to replicate high-dimensional missing dynamical terms on problems with and without the separation of temporal scales.

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Cited by in corpus (6)

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• Kernel-based Prediction of Non-Markovian Time Series
• Deep learning delay coordinate dynamics for chaotic attractors from partial observable data
• Machine Learning-Based Statistical Closure Models for Turbulent Dynamical Systems
• Data-driven model reduction for stochastic Burgers equations
• Error Bounds of the Invariant Statistics in Machine Learning of Ergodic Itô Diffusions