A novel class of stabilized greedy kernel approximation algorithms: Convergence, stability and uniform point distribution T Wenzel, G Santin, B Haasdonk
Journal of Approximation Theory 262, 105508, 2021
37 2021 Analysis of Target Data-Dependent Greedy Kernel Algorithms: Convergence Rates for f -, f /P -Greedy T Wenzel, G Santin, B Haasdonk
Constructive Approximation 57 (1), 45-74, 2023
24 2023 A new certified hierarchical and adaptive RB-ML-ROM surrogate model for parametrized PDEs B Haasdonk, H Kleikamp, M Ohlberger, F Schindler, T Wenzel
SIAM Journal on Scientific Computing 45 (3), A1039-A1065, 2023
15 2023 A full order, reduced order and machine learning model pipeline for efficient prediction of reactive flows P Gavrilenko, B Haasdonk, O Iliev, M Ohlberger, F Schindler, P Toktaliev, ...
International Conference on Large-Scale Scientific Computing, 378-386, 2021
6 2021 Data-driven kernel designs for optimized greedy schemes: A machine learning perspective T Wenzel, F Marchetti, E Perracchione
SIAM Journal on Scientific Computing 46 (1), C101-C126, 2024
4 2024 Stability of convergence rates: Kernel interpolation on non-Lipschitz domains T Wenzel, G Santin, B Haasdonk
arXiv preprint arXiv:2203.12532, 2022
4 2022 Universality and optimality of structured deep kernel networks T Wenzel, G Santin, B Haasdonk
arXiv preprint arXiv:2105.07228, 2021
3 2021 Application of Deep Kernel Models for Certified and Adaptive RB-ML-ROM Surrogate Modeling T Wenzel, B Haasdonk, H Kleikamp, M Ohlberger, F Schindler
arXiv preprint arXiv:2302.14526, 2023
2 2023 Structured deep kernel networks for data-driven closure terms of turbulent flows T Wenzel, M Kurz, A Beck, G Santin, B Haasdonk
International Conference on Large-Scale Scientific Computing, 410-418, 2021
2 2021 Goal‐Oriented Two‐Layered Kernel Models as Automated Surrogates for Surface Kinetics in Reactor Simulations F Döppel, T Wenzel, R Herkert, B Haasdonk, M Votsmeier
Chemie Ingenieur Technik, 2024
1 2024 On the optimality of target-data-dependent kernel greedy interpolation in Sobolev Reproducing Kernel Hilbert Spaces G Santin, T Wenzel, B Haasdonk
arXiv preprint arXiv:2307.09811, 2023
1 2023 Adaptive meshfree solution of linear partial differential equations with PDE-greedy kernel methods T Wenzel, D Winkle, G Santin, B Haasdonk
arXiv preprint arXiv:2207.13971, 2022
1 2022 Biomechanical surrogate modelling using stabilized vectorial greedy kernel methods B Haasdonk, T Wenzel, G Santin, S Schmitt
Numerical Mathematics and Advanced Applications ENUMATH 2019: European …, 2020
1 2020 A comparison study of supervised learning techniques for the approximation of high dimensional functions and feedback control M Oster, L Saluzzi, T Wenzel
arXiv preprint arXiv:2402.01402, 2024
2024 A new method to design energy-conserving surrogate models for the coupled, nonlinear responses of intervertebral discs M Hammer, T Wenzel, G Santin, L Meszaros-Beller, JP Little, B Haasdonk, ...
Biomechanics and Modeling in Mechanobiology, 1-24, 2024
2024 Sharp inverse estimates for radial basis function interpolation: One-to-one correspondence between smoothness and approximation rates T Wenzel
arXiv preprint arXiv:2306.14618, 2023
2023 Two-layered and deep kernels as data-adapted kernels T Wenzel, Y Saleh, A Iske
Bernard HaasdonkProfessor for Numerical MathematicsBestätigte E-Mail-Adresse bei mathematik.uni-stuttgart.de
