Biomechanical surrogate modelling using stabilized vectorial greedy kernel methods
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.
- Publication:
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arXiv e-prints
- Pub Date:
- April 2020
- DOI:
- 10.48550/arXiv.2004.12670
- arXiv:
- arXiv:2004.12670
- Bibcode:
- 2020arXiv200412670H
- Keywords:
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- Mathematics - Numerical Analysis;
- Computer Science - Machine Learning
- E-Print:
- Numerical Mathematics and Advanced Applications ENUMATH 2019