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07/19/2023
On the optimality of target-data-dependent kernel greedy interpolation in Sobolev Reproducing Kernel Hilbert Spaces
Kernel interpolation is a versatile tool for the approximation of functi...
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06/26/2023
Sharp inverse estimates for radial basis function interpolation: One-to-one correspondence between smoothness and approximation rates
While direct statemets for kernel based approximation on domains Ω⊂ℝ^d u...
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02/28/2023
Application of Deep Kernel Models for Certified and Adaptive RB-ML-ROM Surrogate Modeling
In the framework of reduced basis methods, we recently introduced a new ...
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01/19/2023
Data-driven kernel designs for optimized greedy schemes: A machine learning perspective
Thanks to their easy implementation via Radial Basis Functions (RBFs), m...
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07/28/2022
Adaptive meshfree solution of linear partial differential equations with PDE-greedy kernel methods
We consider the meshless solution of PDEs via symmetric kernel collocati...
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03/23/2022
Stability of convergence rates: Kernel interpolation on non-Lipschitz domains
Error estimates for kernel interpolation in Reproducing Kernel Hilbert S...
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05/16/2021
Analysis of target data-dependent greedy kernel algorithms: Convergence rates for f-, f · P- and f/P-greedy
Data-dependent greedy algorithms in kernel spaces are known to provide f...
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05/15/2021
Universality and Optimality of Structured Deep Kernel Networks
Kernel based methods yield approximation models that are flexible, effic...
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04/06/2021
A full order, reduced order and machine learning model pipeline for efficient prediction of reactive flows
We present an integrated approach for the use of simulated data from ful...
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03/25/2021
Structured Deep Kernel Networks for Data-Driven Closure Terms of Turbulent Flows
Standard kernel methods for machine learning usually struggle when deali...
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04/27/2020
Biomechanical surrogate modelling using stabilized vectorial greedy kernel methods
Greedy kernel approximation algorithms are successful techniques for spa...
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11/11/2019