Prof. Dr.

Bernard Haasdonk

Head of Group
Dean of Studies (B.Sc./M.Sc. Mathematik)
Institute of Applied Analysis and Numerical Simulation
Work Group Numerical Mathematics

Contact

+49 711 685-65542
+49 711 685-65507

Pfaffenwaldring 57
70569 Stuttgart
Germany
Room: 7.328

Office Hours

Consultation Hours: on request

For many of the following entries, you may find URLs or electronic versions in my google-scholar profile

2019

Wenzel, T., Santin, G., Haasdonk, B.: A novel class of stabilized greedy kernel approximation algorithms: Convergence, stability & uniform point distribution, arXiv Preprint 1911.04352, 2019. (submitted, preprint)

Denzel, A., Haasdonk, B., Kästner, J.: Gaussian Process Regression for Minimum Energy Path Optimization and Transition State Search, The Journal of Physical Chemistry, 2019. (accepted)

Schmidt, A; Wittwar, D.; Haasdonk, B.: Rigorous and effective a-posteriori error bounds for nonlinear problems – Application to RB methods. ACOM, 2019, (accepted, preprint)

Fehr, J., Haasdonk, B. (Eds.): Model Reduction of Coupled Systems – MORCOS 2018, IUTAM Symposia Proceedings Series, Springer, 2019.

B. Haasdonk: MOR Software, Chapter in P. Benner, W. Schilders, S. Grivet-Talocia, Q. Quarteroni, G. Rozza and L. M. Silveira (Eds.) The Model Reduction Handbook, de Gruyter, 2019. (submitted)

G. Santin, B. Haasdonk: Kernel Methods for Surrogate Modelling, Chapter in P. Benner, W. Schilders, S. Grivet-Talocia, Q. Quarteroni, G. Rozza and L. M. Silveira (Eds.) The Model Reduction Handbook, de Gruyter, 2019. (submitted, preprint arXiv 1907.10556)

Carlberg, K., Brencher, L., Haasdonk, B., Barth, A.: Datadriven time parallelism via forecasting, SISC, SIAM Journal on Scientific Computing, 2019. (accepted, preprint arXiv 1610.09049)

Buchfink, P., Bhatt, A., Haasdonk, B.: Symplectic Model Order Reduction with Non Orthonormal Bases, Math. Comput. Appl., 24(2), 43, 2019. (https://doi.org/10.3390/mca24020043)

Grunert, D., Fehr, J., Haasdonk, B.: Well-scaled, a posteriori error estimation for model order reduction of large second-order mechanical systems, Preprint, University of Stuttgart, 2019. (submitted)

Hamzi, B., Haasdonk, B., Santin, G., Wittwar, D.: Greedy Kernel Methods for Center Manifold Approximation, Proc. ICOSAHOM 2018, Springer, 2019. (preprint arXiv 1810.11329)

Bhatt, A., Fehr, J., Haasdonk, B.: A posteriori Error Estimation in Model Order Reduction of Elastic Multibody Systems with Large Rigid Motion, Proc. MORCOS 2018, Springer, 2019.

2018

Schmidt, A., Haasdonk, B.: Reduced basis approximation of large scale parametric algebraic Riccati equations, ESAIM COCV: Control, Optimisation and Calculus of Variations, 2018.

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. 23, 2018.

C. Dibak, B. Haasdonk, A. Schmidt, F. D¨ urr, K. Rothermel: Enabling interactive mobile simulations through distributed reduced models, Pervasive and Mobile Computing, 45: 19–34, 2018.

Köppl, T., Santin, G., Haasdonk, B., Helmig, R.: Numerical modelling of a peripheral arterial stenosis using dimensionally reduced models and kernel methods, International Journal for Numerical Methods in Biomedical Engineering, e3095, 2018.

Köppel, M., Franzelin, F., Kröker, I., Oladyshkin, S., Santin, G., Wittwar, D., Barth, A., Haasdonk, B., Nowak, W., Pflüger, D., Rohde, C.: Comparison of data-driven uncertainty quantification methods for a carbon dioxide storage benchmark scenario, Computational Geosciences, 1–16, 2018.

Wittwar, D., Santin, G., Haasdonk, B.: Interpolation with uncoupled separable matrix-valued kernels, Dolomites Research Notes on Approximation, 2018.

Maboudi Afkham, B., Bhatt, A., Haasdonk, B., Hesthaven, J.: Symplectic Model-Reduction with a Weighted Inner Product, arXiv:1803.07799v1, 2018.

Santin, G., Wittwar, D., Haasdonk, B.: Greedy Regularized Kernel Interpolation, arXiv:1807.09575, 2018.

Bhatt, A., Fehr, J., Haasdonk, B.: Model Order Reduction of an Elastic Body under Large Rigid Motion, In Proc. Proc. ENUMATH 2017, Voss, Norway, 2018.

Brünnette, T., Santin, G., Haasdonk, B.: Greedy kernel methods for accelerating implicit integrators for parametric ODEs, In Proc. Proc. ENUMATH 2017, Voss, Norway, 2018.

Wittwar, D., Haasdonk, B.: Greedy Algorithms for Matrix-Valued Kernels, In Proc. Proc. ENUMATH 2017, Voss, Norway, 2018.

Fehr, J., Grunert, D., Bhatt, A., Hassdonk, B.: A Sensitivity Study of Error Estimation in Reduced Elastic Multibody Systems, In Proc. of MATHMOD 2018, Vienna, Austria, 2018.

Schmidt, A., Haasdonk, B.: Data-driven surrogates of value functions and applications to feedback control for dynamical systems, In Proc. of MATHMOD 2018, Vienna, Austria, 2018.

2017

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

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

Wittwar, D., Schmidt, A., Haasdonk, B.: Reduced Basis Approximation for the Discrete-time Parametric Algebraic Riccati Equation, SimTech Preprint, University of Stuttgart, 2017.

Alla, A.; Haasdonk, B., Schmidt, A.: Feedback control of parametrized PDEs via model order reduction and dynamic programming principle, University of Stuttgart, 2017. (submitted)

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

Baur, U., Benner, P., Haasdonk, B., Himpe, C., Martini, I., Ohlberger M., Comparison of methods for parametric model order reduction of instationary problems, Chapter in P. Benner, A. Cohen, M. Ohlberger and K. Willcox (Eds.) Model Reduction and Approximation: Theory and Algorithms, SIAM, Philadelphia, 2017.

Dibak, C., Schmidt, A., D¨ urr, F., Haasdonk, B., Rothermel, K.: Server-Assisted Interactive Mobile Simulations for Pervasive Applications, Proceedings of the 15th IEEE International Conference on Pervasive Computing and Communications (PerCom), 2017. (IEEE Mark Weiser Best Paper Award)

Alla, A., Schmidt, A., Haasdonk, B.: Model order reduction approaches for infinite horizon optimal control problems via the HJB equation, In Proc. MoRePaS 2015, 2017.

Haasdonk, B., Santin, G.: Greedy Kernel Approximation for Sparse Surrogate Modelling, in Proc. KOMSO Workshop “Reduced-Order Modeling for Simulation and Optimization”, 2017.

Tempel, P., Schmidt, A., Haasdonk, B., Pott, A.: Application of the Rigid Finite Element Method to the Simulation of Cable-Driven Parallel Robots, In Computational Kinematics, 198–205, Springer International Publishing, 2017.

2016

Dihlmann, M., Haasdonk, B.: A reduced basis Kalman filter for parametrized partial differential equations, ESAIM: Control, Optimisation and Calculus of Variations, EDP Sciences, 22(3):625–669, 2016.

Redeker, M., Haasdonk, B.: A POD-EIM reduced two-scale model for precipitation in porous media, MCMDS, Mathematical and Computer Modelling of Dynamical Systems, 22(4):323–344, 2016.

Garmatter, D.; Haasdonk, B., Harrach, B.: A reduced Landweber Method for Nonlinear Inverse Problems, Inverse Problems, 32:1–21, 2016.

Amsallem, D., Haasdonk, B.: PEBL-ROM: Projection-Error Based Local Reduced-Order Models, AMSES, Advanced Modeling and Simulation in Engineering Sciences, 3, 2016.

Schmidt, A., Haasdonk, B.: Reduced basis method for H2 optimal feedback control problems, Proc. Mathmod 2016, IFAC-PapersOnLine, 49:327–332, 2016.

Antoulas, A. C., Haasdonk, B., Peherstorfer, B.: MORML 2016 Book of Abstracts, University of Stuttgart, 2016.

2015

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

Wirtz, D., Karajan, N., Haasdonk, B.: Surrogate modelling of multiscale models using kernel methods. IJNME, International Journal of Numerical Methods in Engineering, 101:1–28, 2015.

Redeker, M., Haasdonk, B.: A POD-EIM reduced two-scale model for crystal growth. ACOM, Advances in Computational Mathematics, 41:987–1013, 2015.

Maier, I., Haasdonk, B.: Reduced basis approximation and a-posteriori error estimation for the coupled Stokes-Darcy system. ACOM, Advances in Computational Mathematics, 41:1131–1157, 2015.

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

Burkovska, O., Haasdonk, B., Salomon, J., Wohlmuth, B.: Reduced basis methods for pricing options with the Black-Scholes and Heston model, SIAM journal on Financial Mathematics (SIFIN), 6(1):685–712, 2015.

Martini, I., Haasdonk, B.: Output error bounds for the Dirichlet-Neumann reduced basis method. Proc. ENUMATH 2013, 103:437–445, 2015.

Amsallem, D.; Farhat, C., Haasdonk, B.: Editorial: Special Issue on Model Reduction, IJNME, International Journal of Numerical Methods in Engineering, 2015, 102, 931–932,
2015.

2014

Maier, I., Haasdonk, B.: A Dirichlet-Neumann reduced basis method for homogeneous domain decomposition problems. Applied Numerical Mathematics, 78:31–48, 2014.

Wirtz, D., Sorensen, D.C., Haasdonk, B.: A-posteriori error estimation for DEIM reduced nonlinear dynamical systems. SIAM J. Sci. Comp., 36:A311–A338, 2014.

Haasdonk, B., Ohlberger, M.: Wenn die Probleme zahlreicher werden: Reduzierte Basis Methoden f ür effiziente und gesicherte numerische Simulation. GAMM Rundbrief, 1:6–13, 2014.

2013

Haasdonk, B.: Convergence Rates of the POD-Greedy Method. M2AN Math. Model. Numer. Anal., 47:859–873, 2013.

Fehr, J., Fischer, M., Haasdonk, B., Eberhard, P.: Greedy based Approximation of Frequency-weighted Gramian Matrices for Model Reduction in Multibody Dynamics. ZAMM, 93:501–519, 2013.

Haasdonk, B., Urban, K. Wieland, B.: Reduced Basis Methods for Parametrized Partial Differential Equations with Stochastic Influences using the Karhunen-Loeve Expansion. SIAM/ASA Journal on Uncertainty Quantification, 1:79–105, 2013.

Wirtz, D., Haasdonk, B.: An Improved Vectorial Kernel Orthogonal Greedy Algorithm. Dolomites Research Notes on Approximation, 6:83–100, 2013.

Amsallem, D., Haasdonk, B., Rozza, G.: A Conference within a Conference for MOR Researchers. SIAM News, 46:8, 2013.

Kaulmann, S., Haasdonk, B.: Online Greedy Reduced Basis Construction using Dictionaries. Proc. ADMOS 2013.

Dihlmann, M., Haasdonk, B.: Certified Nonlinear Parameter Optimization with Reduced Basis Surrogate Models, Proc. Appl. Math. Mech., 13:3–6, 2013.

2012

Wirtz, D., Haasdonk, B.: Efficient a-posteriori error estimation for nonlinear kernel-based reduced systems. Systems & Control Letters, 61:203–211, 2012.

Drohmann, M., Haasdonk, B., Ohlberger, M.: Reduced Basis Approximation for Nonlinear Parametrized Evolution Equations based on Empirical Operator Interpolation. SISC, SIAM Journal on Scientific Computing, 34(2):A937–A969, 2012.

Waldherr, S., Haasdonk, B.: Efficient Parametric Analysis of the Chemical Master Equation through Model Order Reduction. BMC Systems Biology, 6:81, 2012. (arXiv:1201.3749)

Ruiner, T., Fehr, J., Haasdonk, B., Eberhard, P.: A-posteriori Error Estimators for Second Order Mechanical Systems. Acta Mechanica Sinica, 228(3):854–862, 2012.

Haasdonk, B., Salomon, J., Wohlmuth, B.: A Reduced Basis Method for Parametrized Variational Inequalities. SINUM, SIAM Journal on Numerical Analysis, 50(5):2656–2676, 2012.

Haasdonk, B., Salomon, J., Wohlmuth, B.: A Reduced Basis Method for the Simulation of American Options. ENUMATH Proceedings, 2012. (arXiv:1201.3289)

Drohmann, M., Haasdonk, B., Ohlberger, M.: Reduced Basis Model Reduction of Parametrized Two-phase Flow in Porous Media. Proc of MATHMOD, 2012.

Dihlmann, M., Kaulmann, S., Haasdonk, B.: Online Reduced Basis Construction Procedure for Model Reduction of Parametrized Evolution Systems. Proc. of MATHMOD, 2012.

Wirtz, D., Haasdonk, B.: A-Posteriori Error Estimation for Parametrized Kernel-based Systems. Proc. of MATHMOD, 2012.

Albrecht, F., Haasdonk, B., Kaulmann, S., Ohlberger, M.: The Localized Reduced Basis Multiscale Method. ALGORITMY 2012.

2011

Haasdonk, B., Ohlberger, M.: Efficient Reduced Models and A-Posteriori Error Estimation for Parametrized Dynamical Systems by Offline/Online Decomposition. Mathematical and Computer Modelling of Dynamical Systems, 17(2):145–161, 2011.

Haasdonk, B., Dihlmann, M., Ohlberger, M.: A Training Set and Multiple Bases Generation Approach for Parametrized Model Reduction Based on Adaptive Grids in Parameter Space. Mathematical and Computer Modelling of Dynamical Systems, 17(4):423–442, 2011.

Jung, N., Patera, A.T., Haasdonk, B., Lohmann, B.: Model Order Reduction and Error Estimation with an Application to the Parameter-Dependent Eddy Current Equation. Mathematical and Computer Modelling of Dynamical Systems, 17(6):561–582, 2011.

Kaulmann, S., Ohlberger, M., Haasdonk, B.: A New Local Reduced Basis Discontinuous Galerkin Approach for Heterogeneous Multiscale Problems. Comptes Rendus Mathematique, 349(23):1233–1238, 2011.

Drohmann, M., Haasdonk, B., Kaulmann, S., Ohlberger, M.: A Software Framework for Reduced Basis Methods using Dune-RB and RBMatlab. Proceedings of the Dune User Meeting 2010, Springer, 2011 .

Dihlmann, M., Drohmann, M., Haasdonk, B.: Model Reduction of Parametrized Evolution Problems using the Reduced basis Method with Adaptive Time-Partitioning. Proc. ADMOS 2011.

Drohmann, M., Ohlberger, M, Haasdonk, B.: Adaptive Reduced Basis Methods for Nonlinear Convection-Diffusion Equations. Proc. FVCA 6, 2011.

Haasdonk, B., Lohmann, B.: Editorial: Special Issue on Model Reduction of Parametrized Problems. Mathematical and Computer Modelling of Dynamical Systems, 17(4):295–296, 2011.

Haasdonk, B.: Reduzierte-Basis-Methoden, Vorlesungsskript SS 2011. IANS-Report 2011-004, 2011.

2010

Haasdonk, B.: Effiziente und Gesicherte Modellreduktion für Parametrisierte Dynamische Systeme. at- Automatisierungstechnik, 58(8):468–474, 2010.

Haasdonk, B., Pekalska, E.: Indefinite Kernel Discriminant Analysis. COMPSTAT 2010.

2009

Pekalska, E., Haasdonk, B.: Kernel Discriminant Analysis with Positive Definite and Indefinite Kernels. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(6):1017–1032, 2009.

Jung, N., Haasdonk, B., Kröner D.: Reduced Basis Method for Quadratically Nonlinear Transport Equations. IJCSM, 2(4):334–353, 2009.

Haasdonk, B., Ohlberger, M.: Reduced Basis Method for Explicit Finite Volume Approximations of Nonlinear Conservation Laws. Proc. 12th International Conference on Hyperbolic Problems 2008: Theory, Numerics, Application, 2009.

Drohmann, M., Haasdonk, B., Ohlberger, M.: Reduced Basis Method for Finite Volume Approximation of Evolution Equations on Parametrized Geometries. ALGORITMY 2009, Conference on Scientific Computing, 2009.

Haasdonk, B., Ohlberger, M.: Space-Adaptive Reduced Basis Simulation for Time-Dependent Problems. MATHMOD 2009, 6th Vienna International Conference on Mathematical Modelling, 2009.

Haasdonk, B., Ohlberger, M.: Efficient Reduced Models for Parametrized Dynamical Systems by Offline/Online Decomposition. MATHMOD 2009, 6th Vienna International Conference on Mathematical Modelling, 2009.

Haasdonk, B., Ohlberger, M., Tonn, T., Urban, K. (Eds.): Model Reduction of Parametrized Systems. MoRePaS 09 Book of Abstracts, 16–18 Sept. 2009, Münster, 2009.

Haasdonk, B., Ohlberger, M.: Efficient A-posteriori Error Estimation for Parametrized Reduced Dynamical Systems. In GMA-Fachausschuss 1.30 Tagungsband, 2009.

2008

Haasdonk, B., Ohlberger, M.: Reduced Basis Method for Finite Volume Approximations of Parametrized Linear Evolution Equations. M2AN, Math. Model. Numer. Anal., 42(2):277–302, 2008.

Haasdonk, B., Ohlberger, M., Rozza, G.: A Reduced Basis Method for Evolution Schemes with Parameter-Dependent Explicit Operators. ETNA, Electronic Transactions on Numerical Analysis, 32:145–161, 2008.

Haasdonk, B., Ohlberger, M.: Adaptive Basis Enrichment for the Reduced Basis Method Applied to Finite Volume Schemes. FVCA5, Proc. 5th International Symposium on Finite Volumes for Complex Applications, 2008.

Haasdonk, B., Pekalska, E.: Classification with Kernel Mahalanobis Distances. Proc. of 32nd. GfKl Conference, Advances in Data Analysis, Data Handling and Business Intelligence, 2008.

Haasdonk, B., Pekalska, E.: Indefinite Kernel Fisher Discriminant. Proc. of ICPR 2008, International Conference on Pattern Recognition, 2008.

Fuhrmann, J., Haasdonk, B., Holzbecher, E., Ohlberger, M.: Editorial: Modeling and Simulation of PEM-FC. Journal of Fuel Cell Science and Technology, 5, 2008.

2007

Haasdonk, B., Burkhardt, H.: Invariant Kernels for Pattern Analysis and Machine Learning. Machine Learning, 68:35–61, 2007.

Haasdonk, B., Ohlberger, M.: Basis Construction for Reduced Basis Methods by Adaptive Parameter Grids. Proc. ADMOS 2007, Proc. International Conference on Adaptive Modeling and Simulation, pp. 116–119, 2007.

2006

Peschke, K.-D., Haasdonk, B., Ronneberger, O., Burkhardt, H., Rösch, P., Harz, M., Popp, J.: Using Transformation Knowledge for the Classification of Raman Spectra of Biological Samples. Biomed 2006, Proc. Fourth IASTED International Conference on Biomedical Engineering, 2006.

2005

Haasdonk, B.: Feature Space Interpretation of SVMs with Indefinite Kernels. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(4):482–492, 2005.

Haasdonk, B.: Transformation Knowledge in Pattern Analysis with Kernel Methods – Distance and Integration Kernels. Dissertation, Institut für Informatik, Universität Freiburg, May 2005. Published as ISBN-3-8322-5026-3, Shaker-Verlag, Aachen, 2006.

Haasdonk, B., Vossen, A., Burkhardt, H.: Invariance in Kernel Methods by Haar-Integration Kernels. SCIA 2005, Scandinavian Conference on Image Analysis, pp. 841–851, Springer-Verlag, 2005.

2004

Haasdonk, B., Keysers, D.: Tangent Distance Kernels for Support Vector Machines. ICPR 2002, International Conference on Pattern Recognition, IEEE, 2004.

Haasdonk, B., Halawani, A., Burkhardt, H.: Adjustable Invariant Features by Partial Haar-Integration. ICPR 2004, International Conference on Pattern Recognition, IEEE, 2004.

Haasdonk, B., Bahlmann, C.: Learning with Distance Substitution Kernels. Pattern Recognition – Proc. of the 26th DAGM Symposium, Springer Berlin, 2004.

2003

Haasdonk, B., Ohlberger, M., Rumpf, M., Schmidt, A., Siebert, K.G.: Multiresolution Visualization of Higher Order Adaptive Finite Element Simulations. Computing, 70:181–204, Springer-Verlag Wien, 2003.

Haasdonk, B., Poluru, B.R., Teynor, A.: Presto-Box 1.1 Library Documentation. Interner Bericht 2/03, IIF-LMB, Universität Freiburg, 2003.

Burkhardt, H., Haasdonk, B.: Mustererkennung WS 02/03, ein multimedialer Grundlagenkurs im Hauptstudium Informatik. Vorlesungs-Aufzeichnungs CDs, Institut für Informatik, Universität Freiburg, 2003.

2002

Bahlmann, C., Haasdonk, B., Burkhardt, H.: On-Line Handwriting Recognition with Support Vector Machines – A Kernel Approach. IWFHR-8, Eighth International Workshop on Frontiers in Handwriting Recognition, Aug. 2002. (awarded
with the Best Paper Presentation Award)

2001

Haasdonk, B., Kröner, D., Rohde, C.: Convergence of a Staggered Lax-Friedrichs Scheme for Nonlinear Conservation Laws on Unstructured Two-Dimensional Grids. Numer. Math., 88:459–484, Springer-Verlag Heidelberg, 2001.

2000

Haasdonk, B.: Convergence of a Staggered Lax-Friedrichs Scheme on Unstructured 2D-Grids. Proceedings of the eighth Int. Conf. on Hyperbolic Problems, Vol. II, pp. 475–484, Birkhäuser Verlag Basel, 2000.

1999

Haasdonk, B.: Konvergenz eines Staggered-Lax-Friedrichs-Verfahrens auf unstrukturierten 2D-Gittern. Diplomarbeit, Mathematisches Institut, Universität Freiburg, Oct. 1999.

Geßner, T., Haasdonk, B., Kende, R., Lenz, M., Metscher, M., Neubauer, R., Ohlberger, M., Rosenbaum, W., Rumpf, M., Schwörer, R., Spielberg, M., Weikard, U.: A Procedural Interface for Multiresolutional Visualization of General Numerical Data. Report 28, SFB 256, Bonn, 1999.

Schmidt, A; Wittwar, D.; Haasdonk, B.: Rigorous and effective a-posteriori error bounds for nonlinear problems – Application to RB methods. ACOM, 2019, accepted. (Preprint)
 
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, 23.
 
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.
 
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.
 
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.
 
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.
 
Schmidt, A. & Haasdonk, B.: Reduced basis approximation of large scale parametric algebraic Riccati equations, ESAIM: Control, Optimisation and Calculus of Variations, EDP Sciences, 2017.
 
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.
 
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.
 
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.
 
Haasdonk, B.: Convergence Rates of the POD--Greedy Method, ESAIM: Mathematical Modelling and Numerical Analysis, EDP Sciences, 2013, 47, 859-873.
 
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.
 
Wirtz, D. & Haasdonk, B.: A Vectorial Kernel Orthogonal Greedy Algorithm, Dolomites Res. Notes Approx., 2013, 6, 83-100.
 
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. 
 
Haasdonk, B.; Salomon, J. & Wohlmuth, B.: A Reduced Basis Method for Parametrized Variational Inequalities, SIAM Journal on Numerical Analysis, 2012, 50, 2656-2676.
 
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.
 
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.
 
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.
 
Haasdonk, B. & Ohlberger, M.: Reduced basis method for finite volume approximations of parametrized linear evolution equations, ESAIM: M2AN, 2008, 42, 277-302.
 
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.
 
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. 
 
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.
 
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.

The following is a list of personal and partially former research interests. For an overview of my groups current main focus, see the group site

  • 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

 

See also my google scholar profile.

Citations: 3212 (Google Scholar)

h-index: 26 (Google Scholar)

i10: 53 (Google Scholar)

Publications: 154 (Google Scholar), 39 (DBLP.uni-trier.de), 53 (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.
Awards
 
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.

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

Neurocomputing

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

Datasets

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

To the top of the page