41 citations · 108 across the 19 of their papers we have counts for
4 papers · 1 filter
On the optimality of target-data-dependent kernel greedy interpolation in Sobolev Reproducing Kernel Hilbert Spaces
Gabriele Santin, Tizian Wenzel, Bernard Haasdonk
Kernel interpolation is a versatile tool for the approximation of functions from data, and it can be proven to have some optimality properties when used with kernels related to cer…
Sharp inverse statements for kernel interpolation
Tizian Wenzel
While direct statements for kernel based interpolation on regions are well researched, far less is known about corresponding inverse statements. The availab…
Application of Deep Kernel Models for Certified and Adaptive RB-ML-ROM Surrogate Modeling
Tizian Wenzel, Bernard Haasdonk, Hendrik Kleikamp +2
In the framework of reduced basis methods, we recently introduced a new certified hierarchical and adaptive surrogate model, which can be used for efficient approximation of input-…
Data-driven kernel designs for optimized greedy schemes: A machine learning perspective
Tizian Wenzel, Francesco Marchetti, Emma Perracchione
Thanks to their easy implementation via Radial Basis Functions (RBFs), meshfree kernel methods have been proved to be an effective tool for e.g. scattered data interpolation, PDE c…