This image showsTizian Wenzel


Tizian Wenzel

Research assistant
University of Stuttgart
Institute of Applied Analysis and Numerical Simulation


+49 711 685-62059

Pfaffenwaldring 57
70569 Stuttgart
Room: 7.326

Office Hours

By arrangement


T. Wenzel, G. Santin, and B. Haasdonk. Analysis of target data-dependent greedy
kernel algorithms: Convergence rates for f -, f · P - and f /P -greedy. ArXiv,
(2105.07411), 2021. Submitted.

T. Wenzel, G. Santin, and B. Haasdonk. Universality and Optimality of Structured
Deep Kernel Networks. ArXiv, (2105.07228), 2021. Submitted.

T. Wenzel, M. Kurz, A. Beck, G. Santin, and B. Haasdonk. Structured Deep Kernel
Networks for Data-Driven Closure Terms of Turbulent Flows. ArXiv, (2103.13655),
2021. Submitted.

P. Gavrilenko, B. Haasdonk, O. Iliev, M. Ohlberger, F. Schindler, P. Toktaliev,
T. Wenzel, and M. Youssef. A full order, reduced order and machine learning model
pipeline for efficient prediction of reactive flows. ArXiv, (2104.02800), 2021. Submit-

T. Wenzel, G. Santin, and B. Haasdonk. A novel class of stabilized greedy kernel
approximation algorithms: Convergence, stability and uniform point distribution.
Journal of Approximation Theory, 262:105508, 2021.


B. Haasdonk, T. Wenzel, G. Santin, and S. Schmitt. Biomechanical surrogate mod-
elling using stabilized vectorial greedy kernel methods. In F. J. Vermolen and
C. Vuik, editors, Numerical Mathematics and Advanced Applications ENUMATH
2019. Springer International Publishing, 2021.


  • Summer term 2019: Assistance to "Numerische Grundlagen"
  • Winter term 2019/20: Assistance to "Mathematik für Wirtschafswissenschaftler"
  • Summer term 2020: Assistance to "Numerical methods for differential equations"
  • 2013-2016: Bachelor studies in Physics at the University of Stuttgart
  • 2016-2019: Master studies in Mathematics at the Univerity of Stuttgart
  • 2018: Internship and working student at Daimler AG, Stuttgart
  • since 2019: PhD student/Ressearch associate at the University of Stuttgart
  • Data-based modelling for high-dimensional function approximation, data-analysis, machine learning
  • Deep greedy kernel methods for submodel coupling in fluid- and biomechanics
  • ML-MORE: Maschinelles Lernen und Modellordnungsreduktion zur Vorhersage der Effizienz katalytischer Filter

2019: Graduation award from Robert Bosch GmbH

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