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arXiv · 2004.12670

Biomechanical surrogate modelling using stabilized vectorial greedy kernel methods

Bernard Haasdonk·Tizian Wenzel·Gabriele Santin·Syn Schmitt
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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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Bernard Haasdonk, Tizian Wenzel, Gabriele Santin, Syn Schmitt. 2020-04-27. Biomechanical surrogate modelling using stabilized vectorial greedy kernel methods. https://doi.org/10.1007/978-3-030-55874-1_49

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