Nonlinear dimensionality reduction for parametric problems: a kernel Proper Orthogonal Decomposition (kPOD)

TL;DR

This paper introduces a nonlinear dimensionality reduction method using kernel PCA for parametric problems, leading to more accurate reduced-order models by capturing complex solution manifolds.

Contribution

It proposes a novel nonlinear manifold approach with local tangent spaces, quadratic enrichment, and a physically motivated kernel for improved model reduction.

Findings

Outperforms traditional POD in numerical accuracy

Reduces the dimension of solution manifolds significantly

Enhances the efficiency of parametric problem solutions

Abstract

Reduced-order models are essential tools to deal with parametric problems in the context of optimization, uncertainty quantification, or control and inverse problems. The set of parametric solutions lies in a low-dimensional manifold (with dimension equal to the number of independent parameters) embedded in a large-dimensional space (dimension equal to the number of degrees of freedom of the full-order discrete model). A posteriori model reduction is based on constructing a basis from a family of snapshots (solutions of the full-order model computed offline), and then use this new basis to solve the subsequent instances online. Proper Orthogonal Decomposition (POD) reduces the problem into a linear subspace of lower dimension, eliminating redundancies in the family of snapshots. The strategy proposed here is to use a nonlinear dimensionality reduction technique, namely the kernel Principal Component Analysis (kPCA), in order to find a nonlinear manifold, with an expected much lower dimension, and to solve the problem in this low-dimensional manifold. Guided by this paradigm, the methodology devised here introduces different novel ideas, namely: 1) characterizing the nonlinear manifold using local tangent spaces, where the reduced-order problem is linear and based on the neighbouring snapshots, 2) the approximation space is enriched with the cross-products of the snapshots, introducing a quadratic description, 3) the kernel for kPCA is defined ad-hoc, based on physical considerations, and 4) the iterations in the reduced-dimensional space are performed using an algorithm based on a Delaunay tessellation of the cloud of snapshots in the reduced space. The resulting computational strategy is performing outstandingly in the numerical tests, alleviating many of the problems associated with POD and improving the numerical accuracy.