6 papers · 1 filter
Samplet compression for conditionally positive definite kernels and universal Kriging
Sara Avesani, Rüdiger Kempf, Michael Multerer +1
We present a samplet-based framework for the efficient numerical solution of saddle-point systems arising from conditionally positive definite (CPD) kernel approximation in general…
Kernel-based Operator Learning: Error Analysis, Budget Allocation, and a Physics-Informed Extension
Rüdiger Kempf
We study kernel-based operator learning in a two-stage sampling framework, where an offline kernel regression operator learns a discretized representation of the target operator fr…
Nodal Representations for Kernel-Based Multilevel Interpolation
Lorenz Gollwitzer, Rüdiger Kempf, Holger Wendland
We study the kernel-based multilevel method for approximating or learning a multivariate function from scattered data, motivated in part by recent applications in sparse grid metho…
Numerical Aspects of the Tensor Product Multilevel Method for High-dimensional, Kernel-based Reconstruction on Sparse Grids
Markus Büttner, Rüdiger Kempf, Holger Wendland
This paper investigates the approximation of functions with finite smoothness defined on domains with a Cartesian product structure. The recently proposed tensor product multilevel…
Multiscale scattered data analysis in samplet coordinates
Sara Avesani, Rüdiger Kempf, Michael Multerer +1
We study multiscale scattered data interpolation schemes for globally supported radial basis functions with focus on the Matérn class. The multiscale approximation is constructed t…
On Quasi-Localized Dual Pairs in Reproducing Kernel Hilbert Spaces
Helmut Harbrecht, Rüdiger Kempf, Michael Multerer
In scattered data approximation, the span of a finite number of translates of a chosen radial basis function is used as approximation space and the basis of translates is used for…