We present a general -- i.e., independent of the underlying equation -- registration procedure for parameterized model order reduction. Given the spatial domain $Omega subset mathbb{R}^2$ and the manifold $mathcal{M}= { u_{mu} : mu in mathcal{P} }$ associated with the parameter domain $mathcal{P} subset mathbb{R}^P$ and the parametric field $mu mapsto u_{mu} in L^2(Omega)$, our approach takes as input a set of snapshots ${ u^k }_{k=1}^{n_{rm train}} subset mathcal{M}$ and returns a parameter-dependent bijective mapping ${Phi}: Omega times mathcal{P} to mathbb{R}^2$: the mapping is designed to make the mapped manifold ${ u_{mu} circ {Phi}_{mu}: , mu in mathcal{P} }$ more amenable for linear compression methods. In this work, we extend and further analyze the registration approach proposed in [Taddei, SISC, 2020]. The contributions of the present work are twofold. First, we extend the approach to deal with annular domains by introducing a suitable transformation of the coordinate system. Second, we discuss the extension to general two-dimensional geometries: towards this end, we introduce a spectral element approximation, which relies on a partition ${ Omega_{q} }_{q=1} ^{N_{rm dd}}$ of the domain $Omega$ such that $Omega_1,ldots,Omega_{N_{rm dd}}$ are isomorphic to the unit square. We further show that our spectral element approximation can cope with parameterized geometries. We present rigorous mathematical analysis to justify our proposal; furthermore, we present numerical results for a heat-transfer problem in an annular domain, a potential flow past a rotating symmetric airfoil, and an inviscid transonic compressible flow past a non-symmetric airfoil, to demonstrate the effectiveness of our method.
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