Combined parameter and model reduction of cardiovascular problems by means of active subspaces and POD-Galerkin methods
arXiv:1711.10884 · doi:10.1007/978-3-319-96649-6_8
Abstract
In this chapter we introduce a combined parameter and model reduction methodology and present its application to the efficient numerical estimation of a pressure drop in a set of deformed carotids. The aim is to simulate a wide range of possible occlusions after the bifurcation of the carotid. A parametric description of the admissible deformations, based on radial basis functions interpolation, is introduced. Since the parameter space may be very large, the first step in the combined reduction technique is to look for active subspaces in order to reduce the parameter space dimension. Then, we rely on model order reduction methods over the lower dimensional parameter subspace, based on a POD-Galerkin approach, to further reduce the required computational effort and enhance computational efficiency.
References in corpus (6)
- Finite volume POD-Galerkin stabilised reduced order methods for the parametrised incompressible Navier-Stokes equations
- Data-driven polynomial ridge approximation using variable projection
- A near-stationary subspace for ridge approximation
- Dimension reduction in heterogeneous parametric spaces with application to naval engineering shape design problems
- Weighted reduced order methods for parametrized partial differential equations with random inputs
- Stabilized weighted reduced basis methods for parametrized advection dominated problems with random inputs
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