# A registration method for model order reduction: data compression and geometry reduction

## TL;DR

This paper introduces a general registration method for parameterized model order reduction that aligns solution manifolds to improve linear compression, applicable across various equations and geometries.

## Contribution

It proposes a novel, equation-independent registration technique that enhances model order reduction by optimizing data alignment for better compression.

## Key findings

- Effective in reducing dimensionality of solution manifolds
- Improves linear compression methods for parameterized problems
- Demonstrated success on two-dimensional numerical examples

## Abstract

We propose a general --- i.e., independent of the underlying equation --- registration method for parameterized Model Order Reduction. Given the spatial domain $\Omega \subset \mathbb{R}^d$ and a set of snapshots $\{ u^k \}_{k=1}^{n_{\rm train}}$ over $\Omega$ associated with $n_{\rm train}$ values of the model parameters $\mu^1,\ldots, \mu^{n_{\rm train}} \in \mathcal{P}$, the algorithm returns a parameter-dependent bijective mapping $\boldsymbol{\Phi}: \Omega \times \mathcal{P} \to \mathbb{R}^d$: the mapping is designed to make the mapped manifold $\{ u_{\mu} \circ \boldsymbol{\Phi}_{\mu}: \, \mu \in \mathcal{P} \}$ more suited for linear compression methods. We apply the registration procedure, in combination with a linear compression method, to devise low-dimensional representations of solution manifolds with slowly-decaying Kolmogorov $N$-widths; we also consider the application to problems in parameterized geometries. We present a theoretical result to show the mathematical rigor of the registration procedure. We further present numerical results for several two-dimensional problems, to empirically demonstrate the effectivity of our proposal.