An optimization-based registration approach to geometry reduction
We develop and assess an optimization-based approach to parametric geometry reduction. Given a family of parametric domains, we aim to determine a parametric diffeomorphism $\Phi$ that maps a fixed reference domain $\Omega$ into each element of the family, for different values of the parameter; the ultimate goal of our study is to determine an effective tool for parametric projection-based model order reduction of partial differential equations in parametric geometries. For practical problems in engineering, explicit parameterizations of the geometry are likely unavailable: for this reason, our approach takes as inputs a reference mesh of $\Omega$ and a point cloud $\{y_i^{\rm raw}\}_{i=1}^Q$ that belongs to the boundary of the target domain $V$ and returns a bijection $\Phi$ that approximately maps $\Omega$ in $V$. We propose a two-step procedure: given the point clouds $\{x_j\}_{j=1}^N\subset \partial \Omega$ and $\{y_i^{\rm raw}\}_{i=1}^Q \subset \partial V$, we first resort to a point-set registration algorithm to determine the displacements $\{ v_j \}_{j=1}^N$ such that the deformed point cloud $\{y_j:= x_j+v_j \}_{j=1}^N$ approximates $\partial V$; then, we solve a nonlinear non-convex optimization problem to build a mapping $\Phi$ that is bijective from $\Omega$ in $\mathbb{R}^d$ and (approximately) satisfies $\Phi(x_j) = y_j$ for $j=1,\ldots,N$.We present a rigorous mathematical analysis to justify our approach; we further present thorough numerical experiments to show the effectiveness of the proposed method.