3 papers
cs.LG2024
Finetuning greedy kernel models by exchange algorithms
Tizian Wenzel, Armin Iske
Kernel based approximation offers versatile tools for high-dimensional approximation, which can especially be leveraged for surrogate modeling. For this purpose, both "knot inserti…
math.NA2023
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-…
math.NA2023
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…