Prof. Dr. Bernard Haasdonk | Research Group Haasdonk | University of Stuttgart


Brit Steiner
Pfaffenwaldring 57
70569 Stuttgart




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Prof. Dr. Bernard Haasdonk

Prof. Dr.
Bernard  Haasdonk
Professor for Numerical Mathematics
Dean of Studies (Studiendekan Lehramt Mathematik)

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				Bernard Haasdonk
Phone 0049 711 685 - 65542
Fax 0049 711 685 - 65507
Room Office 7.328
Email address
University of Stuttgart
Institute of Applied Analysis and Numerical Simulation
Pfaffenwaldring 57
D-70569  Stuttgart

Thursdays 11:30-12:30 (not 28. June, not 12. July) or on appointment

Research Interests

For a short overview of my group, see the start page. The following is a list of more detailed and personal interests:

  • Model Reduction
    • Parametrized PDEs
    • Parametrized dynamical systems
    • Reduced basis methods
    • Kernel methods for nonlinear systems
    • Adaptive Basis Generation
    • POD-Greedy procedures
  • Numerical Analysis
    • Evolution schemes, FV, LDG-methods
    • Conservation laws
    • Variational inequalities
    • Inverse Problems
    • Optimization with PDE constraints
    • Optimal control, Feedback control
    • Kernel methods for function approximation / PDEs
    • Greedy Procedures
  • Applications
    • Transport problems, fluid dynamics, single-/two-phase flow
    • Obstacle problems, Option Pricing
    • Geometry parametrization and optimization
    • Multiscale problems
    • Elastic multibody systems
    • Chemical Master Equation
    • Fuel cells, Lithium-Ion cells
  • Scientific Computing
    • Multiresolution visualization
    • Numerical Software development
    • Grape/dune-rb/RBmatlab/KerMor
  • Machine Learning
    • Kernel methods, kernel design
    • Support vector machines
    • Kernel Fisher / Mahalanobis Discriminants
    • Proximity-based learning
  • Pattern Recognition
    • Feature extraction
    • Classifier design
    • Invariance
    • Image processing
    • Handwriting Recognition
    • Raman-Spectra Recognition


A complete list of all publications and a list of recent preprints and miscellaneous items can be found here .

Selected Publications

Fritzen, F.; Haasdonk, B.; Ryckelynck, D. & Schöps, S.: An algorithmic comparison of the Hyper-Reduction and the Discrete Empirical Interpolation Method for a nonlinear thermal problem, Math. Comput. Appl. 2018, University of Stuttgart, 2018, 23. Zeige BibTex

Haasdonk, B. & Santin, G.: Keiper, Winfried and Milde, Anja and Volkwein, Stefan (Eds.), Greedy Kernel Approximation for Sparse Surrogate Modeling, Reduced-Order Modeling (ROM) for Simulation and Optimization: Powerful Algorithms as Key Enablers for Scientific Computing, Springer International Publishing, 2018, 21-45. Zeige BibTex Zeige Abstract

Haasdonk, B.: P. Benner and A. Cohen and M. Ohlberger and K. Willcox (Eds.), Reduced Basis Methods for Parametrized PDEs -- A Tutorial Introduction for Stationary and Instationary Problems, Model Reduction and Approximation: Theory and Algorithms, SIAM, Philadelphia, 2017, 65-136. Zeige BibTex

Martini, I.; Rozza, G. & Haasdonk, B.: Certified Reduced Basis Approximation for the Coupling of Viscous and Inviscid Parametrized Flow Models, Journal of Scientific Computing, 2017. Zeige BibTex

Santin, G. & Haasdonk, B.: Convergence rate of the data-independent P-greedy algorithm in kernel-based approximation, Dolomites Research Notes on Approximation, 2017, 10, 68-78. Zeige BibTex Zeige Abstract

Schmidt, A. & Haasdonk, B.: Reduced basis approximation of large scale parametric algebraic Riccati equations, ESAIM: Control, Optimisation and Calculus of Variations, EDP Sciences, 2017. Zeige BibTex

Dihlmann, M. A. & Haasdonk, B.: Certified PDE-constrained parameter optimization using reduced basis surrogate models for evolution problems, COAP, Computational Optimization and Applications, 2015, 60, 753-787. Zeige BibTex

Kaulmann, S.; Flemisch, B.; Haasdonk, B.; Lie, K.-A. & Ohlberger, M.: The Localized Reduced Basis Multiscale method for two-phase flows in porous media, Internat. J. Numer. Methods Engrg., 2015, 102, 1018-1040. Zeige BibTex

Wirtz, D.; Karajan, N. & Haasdonk, B.: Surrogate Modelling of multiscale models using kernel methods, International Journal of Numerical Methods in Engineering, 2015, 101, 1-28. Zeige BibTex

Haasdonk, B.: Convergence Rates of the POD--Greedy Method, ESAIM: Mathematical Modelling and Numerical Analysis, EDP Sciences, 2013, 47, 859-873. Zeige BibTex Zeige Abstract

Haasdonk, B.; Urban, K. & Wieland, B.: Reduced basis methods for parametrized partial differential equations with stochastic influences using the Karhunen Loeve expansion, SIAM/ASA J. Unc. Quant., 2013, 1, 79-105. Zeige BibTex

Wirtz, D. & Haasdonk, B.: A Vectorial Kernel Orthogonal Greedy Algorithm, Dolomites Res. Notes Approx., 2013, 6, 83-100. Zeige BibTex Zeige Abstract

Drohmann, M.; Haasdonk, B. & Ohlberger, M.: Reduced Basis Approximation for Nonlinear Parametrized Evolution Equations based on Empirical Operator Interpolation, SIAM J. Sci. Comput., 2012, 34, A937-A969. Zeige BibTex Zeige Abstract

Haasdonk, B.; Salomon, J. & Wohlmuth, B.: A Reduced Basis Method for Parametrized Variational Inequalities, SIAM Journal on Numerical Analysis, 2012, 50, 2656-2676. Zeige BibTex

Haasdonk, B.; Dihlmann, M. & Ohlberger, M.: A Training Set and Multiple Basis Generation Approach for Parametrized Model Reduction Based on Adaptive Grids in Parameter Space, Mathematical and Computer Modelling of Dynamical Systems, 2011, 17, 423-442. Zeige BibTex

Haasdonk, B. & Ohlberger, M.: Efficient reduced models and it a posteriori error estimation for parametrized dynamical systems by offline/online decomposition, Math. Comput. Model. Dyn. Syst., 2011, 17, 145-161. Zeige BibTex

Haasdonk, B. & Ohlberger, M.: Reduced basis method for explicit finite volume approximations of nonlinear conservation laws, Hyperbolic problems: theory, numerics and applications, Amer. Math. Soc., 2009, 67, 605-614. Zeige BibTex

Haasdonk, B. & Ohlberger, M.: Reduced basis method for finite volume approximations of parametrized linear evolution equations, ESAIM: M2AN, 2008, 42, 277-302. Zeige BibTex

Haasdonk, B. & Burkhardt, H.: Invariant Kernels for Pattern Analysis and Machine Learning, Machine Learning, IIF-LMB, Universität Freiburg, Institut für Informatik, 2007, 68, 35-61. Zeige BibTex

Haasdonk, B.: Feature Space Interpretation of SVMs with Indefinite Kernels, IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Computer Society, 2005, 27, 482-492. Zeige BibTex

Bahlmann, C.; Haasdonk, B. & Burkhardt, H.: On-line Handwriting Recognition with Support Vector Machines - A Kernel Approach, Proc. of the 8th International Workshop on Frontiers in Handwriting Recognition, IEEE Computer Society, 2002, 49-54. Zeige BibTex Zeige Abstract

Haasdonk, B.; Kröner, D. & Rohde, C.: Convergence of a staggered Lax-Friedrichs scheme for nonlinear conservation laws on unstructured two-dimensional grids, Numer. Math., 2001, 88, 459-484. Zeige BibTex


See the teaching page.

Further information

Bibliometrical Data

See also my google scholar profile.

Citations: 2627 (Google Scholar)

h-index: 24 (Google Scholar)

i10: 48 (Google Scholar)

Publications: 127 (Google Scholar), 30 (, 46 (Zentralblatt MATH)

Erdös Number: 4 (Peter Benner, Carl T. Kelley, Marc A. Berger, Paul Erdös)

Funded Projects

Principal Investigator in a project funded by the State Baden Württemberg within the SimTech Cluster of Excellence (Anschubprojekt): Kernel Approximation for Control and Integration of Dynamical Systems, 2017-2018.
Principal Investigator in a project funded by the DFG within the IRTG 2198: Model Reduction for Soft Tissue Simulation, 2017-2020.

Principal Investigator (with Jun.-Prof. Dr. J. Fehr) in a project funded by the DFG: Certified Model Reduction for Coupled Mechanical Systems, 2017-2019.

Principal Investigator in a project funded by the DFG within the SimTech Cluster of Excellence: Feedback Control of Parametric PDEs with Reduced Basis Surrogate Models, 2014-2017.

Principal Investigator in a project funded by the Baden Württemberg Stiftung gGmbH, MWK-BW (Juniorprofessorenprogramm): RB-Methoden für Heterogene Gebietszerlegung, 2012-2015.

Principal Investigator in a project funded by the Baden Württemberg Stiftung gGmbH, MWK-BW (Juniorprofessorenprogramm): Maschinelles Lernen zur Simulationsbasierten Modellreduktion, 2010-2013.

Principal Investigator in a project funded by the DFG within the SimTech Cluster of Excellence (JP-Anschubprojekt): KerMor: Kernel Methods for Model Order Reduction of Biochemical Systems. , 2010-2012.

Principal Investigator in a project funded by the DFG within the SimTech Cluster of Excellence: RBEvolOpt: Reduced Basis Modelling of Higher-Order Evolution Systems and Application in Optimisation, 2009-2014.

Principal Investigator (with M. Ohlberger) in a project funded by the DFG: Reduzierte Basis Methoden zur Modellreduktion für Nichtlineare Parametrisierte Evolutionsgleichungen, 2009-2012.

Organizer (with M. Ohlberger, T. Tonn and K. Urban) in a workshop funded by the DFG: MoRePaS 09, Model Reduction of Parametrized Systems, Unversity of Münster, September 16-18, 2009.

Principal Investigator in a project funded by the Landesstiftung Baden-Württemberg gGmbH: Modellreduktion zur Simulation von Transportprozessen und Anwendungen in Brennstoffzellen, 2007-2009.

Principal Investigator (with E. Pekalska) in a project funded by the DAAD: Indefinite Kernel Methods and Learning in General Proximity Spaces , 2007-2008.

In charge of BMBF sub-project for Modellbasiertes Design von Brennstoffzellen und Brennstoffzellensystemen: PEMDesign, 2005-2008.

In charge of BMBF sub-project for ULI, Universitärer Lehrverbund Informatik, 2001-2003.


2017: Teaching Award "Beste Aufbauvorlesung" of the Fachgruppe Mathematik of the University of Stuttgart for the lecture "Numerische Mathematik 2".
2017: IEEE PerCom 2017: Mark Weiser Best Paper Award for Dibak, C., Schmidt, A., Dürr, F., Haasdonk, B., Rothermel, K.: Server-Assisted Interactive Mobile Simulation for Pervasive Applications, 2017.
2013: Teaching Award "Beste Grundlagenvorlesung" of the Fachgruppe Mathematik of the University of Stuttgart for the lecture "Numerische Mathematik 1".
2012: Teaching Award "Beste Vertiefungsvorlesung" of the Fachgruppe Mathematik of the University of Stuttgart for the lecture "Reduced Basis Methods".

2009: Best Paper Award for the contribution: Haasdonk, B., Pekalska, E., Classification with Kernel Mahalanobis Distances. Proc. of 32nd. GfKl Conference, Advances in Data Analysis, Data Handling and Business Intelligence, 2008.
2008: Participation in the Awarded Exhibition Hightech Underground 2008

2007: DAAD-ARC research grant

2006: Admittance to the Eliteprogramm für Postdoktorandinnen und Postdoktoranden of the Landesstiftung Baden-Württemberg gGmbH

2004: Prize in SAS Mining Challenge 2003

2002: Best Paper Presentation Award for the contribution: Bahlmann, C., Haasdonk, B., Burkhardt, H., On-Line Handwriting Recognition with Support Vector Machines - A Kernel Approach. IWFHR-8, 2002. (.ps, .pdf)

2000: Förderpreis 2000 des Verbands der Freunde der Universität Freiburg for the best graduation at the Institute of Mathematics.


Professional Activities

Scientific Organizations
EU-MORNET, Management Commitee member of the European Network on Model Reduction.
DMV, German Mathematicians Society

DAGM, German Pattern Recognition Society

IAPR, International Association for Pattern Recognition

DHV, German Association of University Professors and Lecturers

WiR-Ba-Wü, Research network for scientific computing in Baden-Württemberg.
CoSiMOR, Scientific Network on Scale Bridging simulation methods based on order-reduction and co-simulation

Research Visits
5/2016: Massachusetts Institute of Technology, Cambridge, USA
11/2013: Stanford University, California, USA
3/2011: Massachusetts Institute of Technology, Cambridge, USA

10/2009: University of Manchester, Manchester, UK.

8/2009: Ecole Polytechnique Lausanne, Lausanne, Switzerland.

8/2008: University of Manchester, Manchester, UK.

9/2007: University of Manchester, Manchester, UK.

4/2007-7/2007: Massachusetts Institute of Technology, Cambridge, USA.

4/2003: Max Planck Institute for Biological Kybernetics, Tübingen, Germany.

Workshop/Conference Organization
MORCOS 2018, IUTAM Symposium on “Model Order Reduction of Coupled Systems” (MORCOS),
Stuttgart, Germany, May 22–25, 2018
MATHMOD 2018, Minisymposium on “Model Order Reduction”,
Vienna, Austria, February 21–23, 2018
MORML 2016, Workshop on “Data-driven Model Reduction and Machine Learning”,
Stuttgart, Germany, March 30 – April 1, 2016
ENUMATH 2015 Minisymposium on “Hierarchical Model Reduction”,
Ankara, Turkey, September 13–18, 2015.
SIAM CSE 2015 Minisymposium on “Reduced Order Models for PDE-constrained Optimization Problems”,
Salt Lake City, Utah, March 14–18, 2015
OWS 2014 Oberwolfach Seminar “Projection-based Model Reduction: Reduced Basis Methods, Proper Orthogonal Decomposition, and Low Rank Tensor Approximations”,
MFO, Oberwolfach, November 23–29, 2014
ICOSAHOM 2014 Minisymposium on “Recent Advances in Model Reduction for Complex Problems”,
Salt Lake City, Utah, June 23–27, 2014

GAMM 2013, Minisymposium on "Model Order Reduction" at the 89th Annual GAMM Conference
Novi Sad, Serbia, March 18-22, 2013.
SIAM CSE 2013, Minisymposium on "Data-based and Nonlinear Model Order Reduction",
Boston, MA, USA, February 25 - March 1, 2013

IANS Miniworkshop on "Minimum Energy Problems",
Stuttgart, Germany, August 17-18, 2012.

MATHMOD 2012, Minisymposium on "Model Order Reduction" at the 7th Vienna International Conference on Mathematical Modelling
Vienna, Austria, February 15-17, 2012.

CEMRACS 2011, SimTech Workshop on "Current Trends in Computational Fluid Mechanics"
Marseille, France, August 22-24, 2011.

SIAM CSE 2011, Mini-Symposium on "Model Reduction of Nonlinear and Parametrized Problems"
Reno, Nevada, February 28-March 4, 2011.

Workshop on Reduced Basis Methods,
Ulm, December 7-8, 2010.

ECCOMAS CFD 2010, Mini-Symposium on "Model Reduction in Computational Fluid Dynamics"
Lisbon, June 14-17, 2010.

MoRePaS 09, Workshop on Model Reduction of Parametrized Systems
Münster, September 16-18, 2009. (successful DFG funding)

PEMSIM2006, Workshop on Modelling and Simulation of PEM Fuel Cells
Berlin, September 18-20, 2006

Journal Referee Activities

SISC, SIAM Journal on Scientific Computing

SINUM, SIAM Journal on Numerical Analysis
JUQ, SIAM/ASA Journal on Uncertainty Quantification

ESAIM M2AN, Mathematical Modelling and Numerical Analysis

CRAS, Comptes Rendus de l'Acadämie des Sciences

MCMDS, Mathematical and Computer Modelling of Dynamical Systems

ZAMM, Journal of Applied Mathematics and Mechanics
CMAME, Computer Methods in Applied Mechanics and Engineering

IJMM, International Journal of Modern Mathematics

IEEE TPAMI, Transactions on Pattern Analysis and Machine Intelligence

IEEE TIP, Transactions on Image Processing

IEEE TNN, Transactions on Neural Networks

ACM TOIS, Transactions on Information Systems

IEEE TPDS, Transactions on Parallel and Distributed Systems

JMLR, Journal of Machine Learning Research

Neural Computation


Pattern Recognition

Pattern Recognition Letters

Pattern Analysis and Applications

IJPRAI, International Journal of Pattern Recognition and Artificial Intelligence

Information Fusion

Signal Processing

IJNS, International Journal of Neural Systems

EJOR, European Journal of Operational Research

SMCB, IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

SMCC, IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews

TFS, IEEE Transactions on Fuzzy Systems

CES, Chemical Engineering Science


Software Packages

RBMatlab: MATLAB toolbox for Reduced Basis Methods and Model Order Reduction

DUNE, DUNE-FEM, DUNE-RB: Distributed and Unified Numerics Environment

KerMet-Tools: MATLAB toolbox for invariant kernel experiments in pattern analysis.

Presto-Box: Scilab toolbox with basic pattern recognition algorithms.

libsvmTL: C++ SVM template library based on libsvm

VisAmp: plattform independent, visually controlled mp3-player

GRAPE: GRAphical Programming Environment for mathematical visualization


Distance Matrices: Small collection of proximity data used in the DAGM2004 paper.