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

## TL;DR

This paper introduces a nonparametric, kernel-based framework for modeling missing dynamics in systems, which can produce parametric models and accurately estimate autocovariance functions, applicable to complex nonlinear dynamics.

## Contribution

It develops a nonparametric modeling approach using RKHS that can derive parametric models and handle systems with varying time scales.

## Key findings

- Accurately estimates autocovariance in linear Gaussian systems.
- Replicates missing nonlinear dynamical terms in high-dimensional systems.
- Aligns with classical averaging theory for fast-evolving dynamics.

## 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.