Biomechanical surrogate modelling using stabilized vectorial greedy kernel methods

• URL: http://arxiv.org/abs/2004.12670v2
• Date: Tue, 28 Apr 2020 07:25:55 GMT
• Title: Biomechanical surrogate modelling using stabilized vectorial greedy kernel methods
• Authors: Bernard Haasdonk and Tizian Wenzel and Gabriele Santin and Syn Schmitt
• Abstract summary: Greedy kernel approximation algorithms are successful techniques for sparse and accurate data-based modelling and function approximation. We introduce the so called $gamma$-restricted VKOGA, comment on analytical properties and present numerical evaluation on data from a clinically relevant application, the modelling of the human spine.
• Score: 0.2580765958706853
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Greedy kernel approximation algorithms are successful techniques for sparse and accurate data-based modelling and function approximation. Based on a recent idea of stabilization of such algorithms in the scalar output case, we here consider the vectorial extension built on VKOGA. We introduce the so called $\gamma$-restricted VKOGA, comment on analytical properties and present numerical evaluation on data from a clinically relevant application, the modelling of the human spine. The experiments show that the new stabilized algorithms result in improved accuracy and stability over the non-stabilized algorithms.

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