9 citations · 41 across the 18 of their papers we have counts for
4 papers · 1 filter
A novel class of stabilized greedy kernel approximation algorithms: Convergence, stability & uniform point distribution
Tizian Wenzel, Gabriele Santin, Bernard Haasdonk
Kernel based methods provide a way to reconstruct potentially high-dimensional functions from meshfree samples, i.e., sampling points and corresponding target values. A crucial ing…
Deep recurrent Gaussian process with variational Sparse Spectrum approximation
Roman Föll, Bernard Haasdonk, Markus Hanselmann +1
Modeling sequential data has become more and more important in practice. Some applications are autonomous driving, virtual sensors and weather forecasting. To model such systems, s…
Kernel Methods for Surrogate Modeling
Gabriele Santin, Bernard Haasdonk
This chapter deals with kernel methods as a special class of techniques for surrogate modeling. Kernel methods have proven to be efficient in machine learning, pattern recognition…
Symplectic Model Order Reduction with Non-Orthonormal Bases
Patrick Buchfink, Ashish Bhatt, Bernard Haasdonk
Parametric high-fidelity simulations are of interest for a wide range of applications. But the restriction of computational resources renders such models to be inapplicable in a re…