Analysis of parametric models for coupled systems

TL;DR

This paper presents a unified functional analytic framework for parametric models in vector spaces, connecting correlation operators, tensor decompositions, and coupled systems, with applications to vector- and tensor-fields.

Contribution

It introduces a generalised correlation operator framework that unifies various tensor and spectral decompositions for parametric models, including coupled systems.

Findings

Spectral decomposition of correlation and kernel operators is achieved.

Hierarchical tensor decompositions are derived from recursive factorisations.

The framework applies to coupled systems and structured parametric models.

Abstract

In many instances one has to deal with parametric models. Such models in vector spaces are connected to a linear map. The reproducing kernel Hilbert space and affine- / linear- representations in terms of tensor products are directly related to this linear operator. This linear map leads to a generalised correlation operator, in fact it provides a factorisation of the correlation operator and of the reproducing kernel. The spectral decomposition of the correlation and kernel, as well as the associated Karhunen-Lo\`eve or proper orthogonal decomposition are a direct consequence. This formulation thus unifies many such constructions under a functional analytic view. Recursively applying factorisations in higher order tensor representations leads to hierarchical tensor decompositions. This format also allows refinements for cases when the parametric model has more structure. Examples are shown for vector- and tensor-fields with certain required properties. Another kind of structure is the parametric model of a coupled system. It is shown that this can also be reflected in the theoretical framework.