A POD-selective inverse distance weighting method for fast parametrized shape morphing

TL;DR

This paper introduces a fast, efficient shape morphing method combining a selective inverse distance weighting approach with proper orthogonal decomposition to reduce computational costs while maintaining accuracy.

Contribution

It proposes a novel POD-based selective IDW method that improves efficiency in parametrized shape morphing tasks.

Findings

Significant reduction in computational time.

Maintained accuracy in shape deformation.

Effective trade-off between efficiency and precision.

Abstract

Efficient shape morphing techniques play a crucial role in the approximation of partial differential equations defined in parametrized domains, such as for fluid-structure interaction or shape optimization problems. In this paper, we focus on Inverse Distance Weighting (IDW) interpolation techniques, where a reference domain is morphed into a deformed one via the displacement of a set of control points. We aim at reducing the computational burden characterizing a standard IDW approach without significantly compromising the accuracy. To this aim, first we propose an improvement of IDW based on a geometric criterion which automatically selects a subset of the original set of control points. Then, we combine this new approach with a dimensionality reduction technique based on a Proper Orthogonal Decomposition of the set of admissible displacements. This choice further reduces computational costs. We verify the performances of the new IDW techniques on several tests by investigating the trade-off reached in terms of accuracy and efficiency.