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.
Categories: math.NA cs.NA