Dieses Bild zeigt Haasdonk

Prof. Dr.

Bernard Haasdonk

Head of Group
Dean of Studies (Studiendekan Lehramt)
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

Consultation

Consultation Hours: on request

Publications:
  1. 2018

    1. D. Wittwar and B. Haasdonk, “Greedy Algorithms for Matrix-Valued Kernels,” University of Stuttgart, 2018.
    2. T. Brünnette, G. Santin, and B. Haasdonk, “Greedy kernel methods for accelerating implicit integrators for parametric  ODEs,” 2018, vol. Proceedings of ENUMATH 2017.
    3. A. Bhatt and B. Haasdonk, “Certified and structure-preserving model order reduction of EMBS.” 2018.
    4. F. Fritzen, B. Haasdonk, D. Ryckelynck, and S. Schöps, “An algorithmic comparison of the Hyper-Reduction and the Discrete  Empirical Interpolation Method for a nonlinear thermal problem,” Math. Comput. Appl. 2018, vol. 23, no. 1, 2018.
    5. B. M. Afkham, A. Bhatt, B. Haasdonk, and J. S. Hesthaven, “Symplectic Model-Reduction with a Weighted Inner Product,” 2018.
    6. T. Köppl, G. Santin, B. Haasdonk, and R. Helmig, “Numerical modelling of a peripheral arterial stenosis using dimensionally  reduced models and kernel methods,” International Journal for Numerical Methods in Biomedical Engineering, vol. 0, no. ja, p. e3095, 2018.
    7. B. Haasdonk and G. Santin, “Greedy Kernel Approximation for Sparse Surrogate Modeling,” in Reduced-Order Modeling (ROM) for Simulation and Optimization: Powerful  Algorithms as Key Enablers for Scientific Computing, W. Keiper, A. Milde, and S. Volkwein, Eds. Cham: Springer International Publishing, 2018, pp. 21--45.
    8. A. Bhatt, B. Haasdonk, and B. E. Moore, “Structure-preserving Integration and Model Order Reduction.” 2018.
  2. 2017

    1. G. Santin and B. Haasdonk, “Convergence rate of the data-independent P-greedy algorithm in  kernel-based approximation,” Dolomites Research Notes on Approximation, vol. 10, pp. 68--78, 2017.
    2. M. Köppel et al., “Comparison of data-driven uncertainty quantification methods for  a carbon dioxide storage benchmark scenario,” 2017.
    3. C. Dibak, A. Schmidt, F. Dürr, B. Haasdonk, and K. Rothermel, “Server-Assisted Interactive Mobile Simulations for Pervasive Applications,” in Proceedings of the 15th IEEE International Conference on Pervasive  Computing and Communications (PerCom), Kona, Hawaii, USA, 2017, pp. 1--10.
    4. B. Haasdonk, “Reduced Basis Methods for Parametrized PDEs -- A Tutorial Introduction  for Stationary and Instationary Problems,” in Model Reduction and Approximation: Theory and Algorithms, P. Benner, A. Cohen, M. Ohlberger, and K. Willcox, Eds. SIAM, Philadelphia, 2017, pp. 65--136.
    5. D. Wittwar, A. Schmidt, and B. Haasdonk, “Reduced Basis Approximation for the Discrete-time Parametric Algebraic  Riccati Equation,” University of Stuttgart, 2017.
    6. A. Schmidt and B. Haasdonk, “Reduced basis approximation of large scale parametric algebraic Riccati  equations,” ESAIM: Control, Optimisation and Calculus of Variations, 2017.
    7. A. Schmidt and B. Haasdonk, “Data-driven surrogates of value functions and applications to feedback  control for dynamical systems,” University of Stuttgart, 2017.
    8. P. Tempel, A. Schmidt, B. Haasdonk, and A. Pott, “Application of the Rigid Finite Element Method to the Simulation  of Cable-Driven Parallel Robots,” in Computational Kinematics, Springer International Publishing, 2017, pp. 198--205.
    9. A. Alla, B. Haasdonk, and A. Schmidt, “Feedback control of parametrized PDEs via model order reduction and  dynamic programming principle,” University of Stuttgart, 2017.
    10. A. Alla, A. Schmidt, and B. Haasdonk, “Model Order Reduction Approaches for Infinite Horizon Optimal Control  Problems via the HJB Equation,” in Model Reduction of Parametrized Systems, P. Benner, M. Ohlberger, A. Patera, G. Rozza, and K. Urban, Eds. Cham: Springer International Publishing, 2017, pp. 333--347.
    11. I. Martini, G. Rozza, and B. Haasdonk, “Certified Reduced Basis Approximation for the Coupling of Viscous  and Inviscid Parametrized Flow Models,” Journal of Scientific Computing, 2017.
  3. 2016

    1. A. C. Antoulas, B. Haasdonk, and B. Peherstorfer, MORML 2016 Book of Abstracts. University of Stuttgart, 2016.
    2. K. Carlberg, L. Brencher, B. Haasdonk, and A. Barth, “Data-driven time parallelism via forecasting,” 2016.
    3. D. Amsallem and B. Haasdonk, “PEBL-ROM: Projection-Error Based Local Reduced-Order Models,” AMSES, Advanced Modeling and Simulation in Engineering Sciences, vol. 3, no. 6, 2016.
    4. M. Dihlmann and B. Haasdonk, “A REDUCED BASIS KALMAN FILTER FOR PARAMETRIZED PARTIAL DIFFERENTIAL    EQUATIONS,” ESAIM-CONTROL OPTIMISATION AND CALCULUS OF VARIATIONS, vol. 22, no. 3, pp. 625–669, 2016.
    5. A. Schmidt and B. Haasdonk, “Reduced basis method for H2 optimal feedback control problems,” IFAC-PapersOnLine, vol. 49, no. 8, pp. 327–332, 2016.
    6. D. Garmatter, B. Haasdonk, and B. Harrach, “A reduced basis Landweber method for nonlinear inverse problems,” INVERSE PROBLEMS, vol. 32, no. 3, 2016.
    7. U. Baur, P. Benner, B. Haasdonk, C. Himpe, I. Maier, and M. Ohlberger, “Comparison of methods for parametric model order reduction of instationary  problems,” in Model Reduction and Approximation for Complex Systems, P. Benner, A. Cohen, M. Ohlberger, and K. Willcox, Eds. Birkhäuser Publishing, 2016.
    8. D. Garmatter, B. Haasdonk, and B. Harrach, “A reduced Landweber Method for Nonlinear Inverse Problems,” Inverse Problems, vol. 32, no. 3, pp. 1--21, 2016.
    9. M. Redeker and B. Haasdonk, “A POD-EIM reduced two-scale model for precipitation in porous media,” MCMDS, Mathematical and Computer Modelling of Dynamical Systems, 2016.
    10. F. Fritzen, B. Haasdonk, D. Ryckelynck, and S. Schöps, “An algorithmic comparison of the Hyper-Reduction and the Discrete  Empirical Interpolation Method for a nonlinear thermal problem,” University of Stuttgart, 2016.
  4. 2015

    1. D. Wirtz, N. Karajan, and B. Haasdonk, “Surrogate modeling of multiscale models using kernel methods,” INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING, vol. 101, no. 1, pp. 1–28, 2015.
    2. I. Martini and B. Haasdonk, “Output Error Bounds for the Dirichlet-Neumann Reduced Basis Method,” in Numerical Mathematics and Advanced Applications - ENUMATH 2013, 2015, vol. 103, pp. 437--445.
    3. M. Dihlmann and B. Haasdonk, “A reduced basis Kalman filter for parametrized partial differential  equations,” ESAIM: Control, Optimisation and Calculus of Variations, 2015.
    4. O. Burkovska, B. Haasdonk, J. Salomon, and B. Wohlmuth, “Reduced basis methods for pricing options with the Black-Scholes  and Heston model,” SIAM journal on Financial Mathematics (SIFIN), no. 1408.1220, 2015.
    5. I. Martini, G. Rozza, and B. Haasdonk, “Reduced basis approximation and a-posteriori error estimation for  the coupled Stokes-Darcy system,” Advances in Computational Mathematics, vol. 41, no. 5, pp. 1131--1157, 2015.
    6. M. Redeker and B. Haasdonk, “A POD-EIM reduced two-scale model for crystal growth,” Advances in Computational Mathematics, vol. 41, no. 5, pp. 987--1013, 2015.
    7. A. Schmidt, M. Dihlmann, and B. Haasdonk, “Basis generation approaches for a reduced basis linear quadratic  regulator,” in Proc. MATHMOD 2015 - 8th Vienna International Conference on Mathematical  Modelling, 2015, pp. 713--718.
    8. M. A. Dihlmann and B. Haasdonk, “Certified PDE-constrained parameter optimization using reduced  basis surrogate models for evolution problems,” COAP, Computational Optimization and Applications, vol. 60, no. 3, pp. 753--787, 2015.
    9. D. Amsallem, C. Farhat, and B. Haasdonk, “Special Issue on Model Reduction,” INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING, vol. 102, no. 5, SI, pp. 931–932, 2015.
    10. O. Burkovska, B. Haasdonk, J. Salomon, and B. Wohlmuth, “Reduced Basis Methods for Pricing Options with the Black-Scholes and    Heston Models,” SIAM JOURNAL ON FINANCIAL MATHEMATICS, vol. 6, no. 1, pp. 685–712, 2015.
    11. S. Kaulmann, B. Flemisch, B. Haasdonk, K.-A. Lie, and M. Ohlberger, “The Localized Reduced Basis Multiscale method for two-phase flows  in porous media,” Internat. J. Numer. Methods Engrg., vol. 102, pp. 1018--1040, 2015.
    12. D. Wirtz, N. Karajan, and B. Haasdonk, “Surrogate Modelling of multiscale models using kernel methods,” International Journal of Numerical Methods in Engineering, vol. 101, no. 1, pp. 1--28, 2015.
    13. D. Amsallem, C. Farhat, and B. Haasdonk, “Editorial: Special Issue on Modelling Reduction,” IJNME, International Journal of Numerical Methods in Engineering, vol. 102, no. 5, pp. 931--932, 2015.
  5. 2014

    1. S. Kaulmann, B. Flemisch, B. Haasdonk, K.-A. Lie, and M. Ohlberger, “The Localized Reduced Basis Multiscale method for two-phase flows  in porous media,” arXiv.org, 2014.
    2. B. Haasdonk and M. Ohlberger, “Wenn die Probleme zahlreicher werden: Reduzierte Basis Methoden  für effiziente und gesicherte numerische Simulation,” GAMM Rundbrief, vol. 2014, no. 1, pp. 6–13, 2014.
    3. B. Haasdonk, “Reduced Basis Methods for Parametrized PDEs -- A Tutorial Introduction  for Stationary and Instationary Problems,” IANS, University of Stuttgart, Germany, 2014.
    4. D. Wirtz, D. C. Sorensen, and B. Haasdonk, “A-posteriori error estimation for DEIM reduced nonlinear dynamical  systems,” SIAM J. Sci. Comp., vol. 36, no. 2, pp. A311--A338, 2014.
    5. I. Maier and B. Haasdonk, “A Dirichlet-Neumann reduced basis method for homogeneous domain  decomposition problems,” Applied Numerical Mathematics, vol. 78, pp. 31--48, 2014.
  6. 2013

    1. D. Amsallem, B. Haasdonk, and G. Rozza, “A Conference within a Conference for MOR Researchers,” SIAM News, vol. 46, no. 6, p. 8, 2013.
    2. J. Fehr, M. Fischer, B. Haasdonk, and P. Eberhard, “Greedy-based Approximation of Frequency-weighted Gramian Matrices  for Model Reduction in Multibody Dynamics,” ZAMM, vol. 93, no. 8, pp. 501–519, 2013.
    3. M. A. Dihlmann and B. Haasdonk, “Certified PDE-constrained parameter optimization using reduced basis  surrogate models for evolution problems,” University of Stuttgart (The final publication is available at Springer  via http://dx.doi.org/10.1007/s10589-014-9697-1), 2013.
    4. S. Kaulmann and B. Haasdonk, “Online Greedy Reduced Basis Construction Using Dictionaries,” in VI International Conference on Adaptive Modeling and Simulation (ADMOS  2013), Lisbon, Portugal, 2013, pp. 365--376.
    5. B. Haasdonk, K. Urban, and B. Wieland, “Reduced basis methods for parametrized partial differential equations  with stochastic influences using the Karhunen Loeve expansion,” SIAM/ASA J. Unc. Quant., vol. 1, pp. 79–105, 2013.
    6. B. Haasdonk, “Convergence Rates of the POD--Greedy Method,” ESAIM: Mathematical Modelling and Numerical Analysis, vol. 47, no. 3, pp. 859--873, 2013.
    7. D. Wirtz and B. Haasdonk, “An Improved Vectorial Kernel Orthogonal Greedy Algorithm,” Dolomites Research Notes on Approximation, vol. 6, pp. 83–100, 2013.
    8. D. Wirtz and B. Haasdonk, “A Vectorial Kernel Orthogonal Greedy Algorithm,” Dolomites Res. Notes Approx., vol. 6, pp. 83–100, 2013.
    9. M. Dihlmann and B. Haasdonk, “Certified Nonlinear Parameter Optimization with Reduced Basis Surrogate  Models,” PAMM, Proc. Appl. Math. Mech., Special Issue: 84th Annual Meeting  of the International Association of Applied Mathematics and Mechanics  (GAMM), Novi Sad 2013; Editors: L. Cvetković, T. Atanacković and  V. Kostić, vol. 13, no. 1, pp. 3–6, 2013.
  7. 2012

    1. D. Wirtz and B. Haasdonk, “An Improved Vectorial Kernel Orthogonal Greedy Algorithm,” University of Stuttgart, 2012.
    2. B. Haasdonk, J. Salomon, and B. Wohlmuth, “A Reduced Basis Method for the Simulation of American Options,” in ENUMATH 2011 Proceedings, 2012.
    3. S. Waldherr and B. Haasdonk, “Efficient Parametric Analysis of the Chemical Master Equation through  Model Order Reduction,” BMC Systems Biology, vol. 6, p. 81, 2012.
    4. M. Dihlmann, S. Kaulmann, and B. Haasdonk, “Online Reduced Basis Construction Procedure for Model Reduction of  Parametrized Evolution Systems,” in Proc. MATHMOD 2012 - 7th Vienna International Conference on Mathematical  Modelling, 2012.
    5. M. Drohmann, B. Haasdonk, and M. Ohlberger, “Reduced Basis Model Reduction of Parametrized Two-phase Flow in Porous  Media,” in Proc. MATHMOD 2012 - 7th Vienna International Conference on Mathematical  Modelling, 2012.
    6. D. Wirtz, D. C. Sorensen, and B. Haasdonk, “A-posteriori error estimation for DEIM reduced nonlinear dynamical  systems,” University of Stuttgart, 2012.
    7. B. Haasdonk, J. Salomon, and B. Wohlmuth, “A Reduced Basis Method for Parametrized Variational Inequalities,” University of Stuttgart, 2012.
    8. M. Drohmann, B. Haasdonk, and M. Ohlberger, “A Software Framework for Reduced Basis Methods Using DUNE-RB and  RBMATLAB,” in Advances in DUNE: Proceedings of the DUNE User Meeting, Held in October  6th-8th 2010 in Stuttgart, Germany, A. Dedner, B. Flemisch, and R. Klöfkorn, Eds. Springer, 2012.
    9. F. Albrecht, B. Haasdonk, S. Kaulmann, and M. Ohlberger, “The Localized Reduced Basis Multiscale Method,” in Algoritmy 2012, 2012, pp. 393--403.
    10. D. Wirtz, N. Karajan, and B. Haasdonk, “Model order reduction of multiscale models using kernel methods,” SRC SimTech, University of Stuttgart, Germany, 2012.
    11. T. Ruiner, J. Fehr, B. Haasdonk, and P. Eberhard, “A-posteriori error estimation for second order mechanical systems,” Acta Mechanica Sinica, vol. 28(3), pp. 854–862, 2012.
    12. M. Drohmann, B. Haasdonk, and M. Ohlberger, “Reduced Basis Approximation for Nonlinear Parametrized Evolution  Equations based on Empirical Operator Interpolation,” SIAM J. Sci. Comput., vol. 34, no. 2, pp. A937–A969, 2012.
    13. D. Wirtz and B. Haasdonk, “A-posteriori error estimation for parameterized kernel-based systems,” in Proc. MATHMOD 2012 - 7th Vienna International Conference on Mathematical  Modelling, 2012.
    14. D. Wirtz and B. Haasdonk, “Efficient a-posteriori error estimation for nonlinear kernel-based  reduced systems,” Systems and Control Letters, vol. 61, no. 1, pp. 203–211, 2012.
  8. 2011

    1. M. Dihlmann, M. Drohmann, and B. Haasdonk, “Model Reduction of Parametrized Evolution Problems using the Reduced  basis Method with Adaptive Time-Partitioning,” in Proc. of ADMOS 2011, 2011.
    2. B. Haasdonk, M. Dihlmann, and M. Ohlberger, “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, vol. 17, pp. 423--442, 2011.
    3. B. Haasdonk, “Reduzierte-Basis-Methoden, Vorlesungsskript SS 2011,” University of Stuttgart, 2011–004, 2011.
    4. B. Haasdonk and B. Lohmann, “Special Issue on ‘“Model Order Reduction of Parametrized Problems,”’” Mathematical and Computer Modelling of Dynamical Systems, vol. 17, no. 4, pp. 295--296, 2011.
    5. S. Kaulmann, M. Ohlberger, and B. Haasdonk, “A new local reduced basis discontinuous Galerkin approach for heterogeneous  multiscale problems,” Comptes Rendus Mathematique, vol. 349, no. 23–24, pp. 1233--1238, 2011.
    6. B. Haasdonk and M. Ohlberger, “Efficient reduced models and a posteriori error estimation  for parametrized dynamical systems by offline/online decomposition,” Math. Comput. Model. Dyn. Syst., vol. 17, no. 2, pp. 145--161, 2011.
    7. M. Drohmann, B. Haasdonk, and M. Ohlberger, “Adaptive Reduced Basis Methods for Nonlinear Convection-Diffusion  Equations,” in In Proc. FVCA6, 2011.
    8. N. Jung, A. T. Patera, B. Haasdonk, and B. Lohmann, “Model Order Reduction and Error Estimation with an Application to  the Parameter-Dependent Eddy Current Equation,” Mathematical and Computer Modelling of Dynamical Systems, vol. 17, no. 4, pp. 561--582, 2011.
  9. 2010

    1. B. Haasdonk, “Effiziente und Gesicherte Modellreduktion für Parametrisierte Dynamische  Systeme.,” at - Automatisierungstechnik, vol. 58, no. 8, pp. 468--474, 2010.
    2. E. Pekalska and B. Haasdonk, “Indefinite Kernel Discriminant Analysis,” in Proc. COMPSTAT 2010, International Conference on Computational Statistics, 2010.
  10. 2009

    1. E. Pekalska and B. Haasdonk, “Kernel Discriminant Analysis with Positive Definite and Indefinite  Kernels,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 6, pp. 1017–1032, 2009.
    2. B. Haasdonk, M. Ohlberger, T. Tonn, and K. Urban, MoRePaS 2009 Book of Abstracts. University of Münster, 2009.
    3. B. Haasdonk and M. Ohlberger, “Efficient a-posteriori Error Estimation for Parametrized Reduced  Dynamical Systems,” in GMA-Fachaussschuss 1.30, Tagungsband, 2009.
    4. B. Haasdonk and M. Ohlberger, “Space-Adaptive Reduced Basis Simulation for Time-Dependent Problems,” in Proc. MATHMOD 2009, 6th Vienna International Conference on Mathematical  Modelling, 2009.
    5. B. Haasdonk and M. Ohlberger, “Efficient Reduced Models for Parametrized Dynamical Systems by Offline/Online  Decomposition,” in Proc. MATHMOD 2009, 6th Vienna International Conference on Mathematical  Modelling, 2009.
    6. B. Haasdonk and M. Ohlberger, “Reduced basis method for explicit finite volume approximations of  nonlinear conservation laws,” in Hyperbolic problems: theory, numerics and applications, vol. 67, Providence, RI: Amer. Math. Soc., 2009, pp. 605--614.
    7. N. Jung, B. Haasdonk, and D. Kröner, “Reduced Basis Method for Quadratically Nonlinear Transport Equations,” IJCSM, vol. 2, no. 4, pp. 334–353, 2009.
  11. 2008

    1. B. Haasdonk and M. Ohlberger, “Adaptive basis enrichment for the reduced basis method applied to  finite volume schemes,” in Finite volumes for complex applications V, ISTE, London, 2008, pp. 471--478.
    2. B. Haasdonk and E. Pekalska, “Indefinite Kernel Fisher Discriminant,” in Proc. ICPR 2008, International Conference on Pattern Recognition, 2008.
    3. E. Pekalska and B. Haasdonk, “Kernel Quadratic Discriminant Analysis with Positive and Indefinite  Kernels,” University of Münster, 06/08, 2008.
    4. B. Haasdonk and M. Ohlberger, “Reduced basis method for finite volume approximations of parametrized  linear evolution equations,” ESAIM: M2AN, vol. 42, no. 2, pp. 277--302, 2008.
    5. M. Drohmann, B. Haasdonk, and M. Ohlberger, “Reduced Basis Method for Finite Volume Approximation of Evolution  Equations on Parametrized Geometries,” in Proceedings of ALGORITMY 2009, 2008, pp. 111--120.
    6. B. Haasdonk and E. Pekalska, “Classification with Kernel Mahalanobis Distances,” in Proc. of 32nd. GfKl Conference, Advances in Data Analysis, Data Handling  and Business Intelligence, 2008.
    7. B. Haasdonk, M. Ohlberger, and G. Rozza, “A Reduced Basis Method for Evolution Schemes with Parameter-Dependent  Explicit Operators,” ETNA, Electronic Transactions on Numerical Analysis, vol. 32, pp. 145--161, 2008.
  12. 2007

    1. B. Haasdonk, M. Ohlberger, and G. Rozza, “A Reduced Basis Method for Evolution Schemes with Parameter-Dependent  Explicit Operators,” University of Münster, 09/07-N, FB 10, 2007.
    2. B. Haasdonk and M. Ohlberger, “Basis Construction for Reduced Basis Methods By Adaptive Parameter  Grids,” in Proc. International Conference on Adaptive Modeling and Simulation,  ADMOS 2007, 2007.
    3. B. Haasdonk and H. Burkhardt, “Invariant Kernels for Pattern Analysis and Machine Learning,” Machine Learning, vol. 68, pp. 35--61, 2007.
    4. J. Fuhrmann, B. Haasdonk, E. Holzbecher, and M. Ohlberger, “Guest Editorial for Special Issue on Modelling and Simulation of  PEM-FC,” Journal of Fuel Cell Science and Technology, 2007.
    5. B. Haasdonk and H. Burkhardt, “Classification with Invariant Distance Substitution Kernels,” in Proc. of 31st GfKl Conference, Data Analysis, Machine Learning, and  Applications, 2007.
  13. 2006

    1. B. Haasdonk and M. Ohlberger, “Reduced Basis Method for Finite Volume Approximations of Parametrized  Evolution Equations,” University of Freiburg, Institute of Applied Mathematics, 12/2006, 2006.
    2. K.-D. Peschke et al., “Using Transformation Knowledge for the Classification of Raman  Spectra of Biological Samples,” in BIOMED 2006, Proc. of the 4th IASTED International Conference on  Biomedical Engineering, 2006, pp. 288–293.
  14. 2005

    1. B. Haasdonk, “Transformation Knowledge in Pattern Analysis with Kernel Methods,  Distance and Integration Kernels,” PhD dissertation, Albert-Ludwigs-Universität, Freiburg im Breisgau, Fakultät für  Angewandte Wissenschaften, 2005.
    2. B. Haasdonk, “Feature Space Interpretation of SVMs with Indefinite Kernels,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 4, pp. 482–492, 2005.
    3. B. Haasdonk, A. Vossen, and H. Burkhardt, “Invariance in Kernel Methods by Haar-Integration Kernels,” in Proceedings of the 14th Scandinavian Conference on Image Analysis, 2005.
  15. 2004

    1. B. Haasdonk and C. Bahlmann, “Learning with Distance Substitution Kernels,” in Pattern Recognition - Proceedings of the 26th DAGM Symposium, 2004, pp. 220–227.
    2. B. Haasdonk, A. Halawani, and H. Burkhardt, “Adjustable invariant features by partial Haar-integration,” in Proceedings of the 17th International Conference on Pattern Recognition, 2004, vol. 2, no. 2, pp. 769–774.
  16. 2003

    1. B. Haasdonk, B. R. Poluru, and A. Teynor, “Presto-Box 1.1 Library Documentation,” IIF-LMB, Universität Freiburg, 2/03, 2003.
    2. B. Haasdonk, M. Ohlberger, M. Rumpf, A. Schmidt, and K. G. Siebert, “Multiresolution Visualization of Higher Order Adaptive Finite Element  Simulations,” Computing, vol. 70, no. 3, pp. 181–204, 2003.
    3. H. Burkhardt and B. Haasdonk, “Mustererkennung WS 02/03, ein multimedialer Grundlagenkurs im  Hauptstudium Informatik.” 2003.
  17. 2002

    1. C. Bahlmann, B. Haasdonk, and H. Burkhardt, “On-line Handwriting Recognition with Support Vector Machines - A  Kernel Approach,” in Proc. of the 8th International Workshop on Frontiers in Handwriting  Recognition, 2002, pp. 49--54.
    2. B. Haasdonk and D. Keysers, “Tangent Distance Kernels for Support Vector Machines,” in Proceedings of the 16th International Conference on Pattern Recognition, 2002, vol. 2, pp. 864–868.
  18. 2001

    1. B. Haasdonk, D. Kröner, and C. Rohde, “Convergence of a staggered Lax-Friedrichs scheme for nonlinear  conservation laws on unstructured two-dimensional grids,” Numer. Math., vol. 88, no. 3, pp. 459--484, 2001.
    2. B. Haasdonk, M. Ohlberger, M. Rumpf, A. Schmidt, and K.-G. Siebert, “h-p-Multiresolution Visualization of Adaptive Finite Element Simulations,” Mathematics Department, University of Freiburg, Preprint 01-26, 2001.
    3. B. Haasdonk, D. Kröner, and C. Rohde, “Convergence of a staggered Lax-Friedrichs scheme for nonlinear  conservation laws on unstructured two-dimensional grids,” Numer. Math., vol. 88, no. 3, pp. 459--484, 2001.
  19. 2000

    1. B. Haasdonk, “Convergence of a Staggered Lax-Friedrichs Scheme on Unstructured  2D-Grids,” in HYP 2000, Proceedings of the 8th International Conference on Hyperbolic  Problems, 2000, vol. 2, pp. 475--484.
  20. 1999

    1. T. Geßner et al., “A Procedural Interface for Multiresolutional Visualization of General  Numerical Data,” University of Bonn, 28, 1999.
    2. B. Haasdonk, “Konvergenz eines Staggered Lax-Friedrichs Verfahrens auf unstrukturierten  2D Gittern,” Master thesis, Universität Freiburg, Abteilung für Angewandte Mathematik, 1999.
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.
 
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: 2834 (Google Scholar)

h-index: 24 (Google Scholar)

i10: 48 (Google Scholar)

Publications: 140 (Google Scholar), 35 (DBLP.uni-trier.de), 41 (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.