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

In this paper, we consider modeling missing dynamics with a non-Markovian transition density, 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 from this nonparametric modeling formulation is in the form of parametric models. This suggests that the successes of various parametric modeling approaches that were proposed in various domain 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 linear and Gaussian case without temporal scale gap, we will show that the proposed closure model using the non-Markovian transition density 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 separation of temporal scales.