Publications

Publications of our group

Note

This page is currently under construction, until the coupling to the PUMA bibliography system is realized. Please refer to the publication list of Prof. Dr. B. Haasdonk until then.

Publications

  1. 2018

    1. B. M. Afkham, A. Bhatt, B. Haasdonk, and J. S. Hesthaven, “Symplectic Model-Reduction with a Weighted Inner Product,” 2018.
    2. A. Bhatt and B. Haasdonk, “Certified and structure-preserving model order reduction of EMBS.” 2018.
    3. A. Bhatt, B. Haasdonk, and B. E. Moore, “Structure-preserving Integration and Model Order Reduction.” 2018.
    4. T. Brünnette, G. Santin, and B. Haasdonk, “Greedy kernel methods for accelerating implicit integrators for parametric  ODEs,” 2018, vol. Proceedings of ENUMATH 2017.
    5. 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.
    6. 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.
    7. 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.
    8. D. Wittwar and B. Haasdonk, “Greedy Algorithms for Matrix-Valued Kernels,” University of Stuttgart, 2018.
  2. 2017

    1. A. Alla, B. Haasdonk, and A. Schmidt, “Feedback control of parametrized PDEs via model order reduction and  dynamic programming principle,” University of Stuttgart, 2017.
    2. 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.
    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. M. Köppel et al., “Comparison of data-driven uncertainty quantification methods for  a carbon dioxide storage benchmark scenario,” 2017.
    6. 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.
    7. 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.
    8. A. Schmidt and B. Haasdonk, “Data-driven surrogates of value functions and applications to feedback  control for dynamical systems,” University of Stuttgart, 2017.
    9. A. Schmidt and B. Haasdonk, “Reduced basis approximation of large scale parametric algebraic Riccati  equations,” ESAIM: Control, Optimisation and Calculus of Variations, 2017.
    10. 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.
    11. D. Wittwar, A. Schmidt, and B. Haasdonk, “Reduced Basis Approximation for the Discrete-time Parametric Algebraic  Riccati Equation,” University of Stuttgart, 2017.
  3. 2016

    1. 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.
    2. A. C. Antoulas, B. Haasdonk, and B. Peherstorfer, MORML 2016 Book of Abstracts. University of Stuttgart, 2016.
    3. 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.
    4. K. Carlberg, L. Brencher, B. Haasdonk, and A. Barth, “Data-driven time parallelism via forecasting,” 2016.
    5. 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.
    6. 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.
    7. 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.
    8. D. Garmatter, B. Haasdonk, and B. Harrach, “A reduced basis Landweber method for nonlinear inverse problems,” INVERSE PROBLEMS, vol. 32, no. 3, 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. A. Schmidt and B. Haasdonk, “Reduced basis method for H2 optimal feedback control problems,” IFAC-PapersOnLine, vol. 49, no. 8, pp. 327–332, 2016.
  4. 2015

    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. M. Dihlmann and B. Haasdonk, “A reduced basis Kalman filter for parametrized partial differential  equations,” ESAIM: Control, Optimisation and Calculus of Variations, 2015.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    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. 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.
  5. 2014

    1. 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.
    2. B. Haasdonk, “Reduced Basis Methods for Parametrized PDEs -- A Tutorial Introduction  for Stationary and Instationary Problems,” IANS, University of Stuttgart, Germany, 2014.
    3. 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.
    4. 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.
    5. 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.
  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. 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.
    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. 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.
    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. 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.
    8. D. Wirtz and B. Haasdonk, “An Improved Vectorial Kernel Orthogonal Greedy Algorithm,” Dolomites Research Notes on Approximation, vol. 6, pp. 83–100, 2013.
    9. D. Wirtz and B. Haasdonk, “A Vectorial Kernel Orthogonal Greedy Algorithm,” Dolomites Res. Notes Approx., vol. 6, pp. 83–100, 2013.
  7. 2012

    1. F. Albrecht, B. Haasdonk, S. Kaulmann, and M. Ohlberger, “The Localized Reduced Basis Multiscale Method,” in Algoritmy 2012, 2012, pp. 393--403.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. B. Haasdonk, J. Salomon, and B. Wohlmuth, “A Reduced Basis Method for the Simulation of American Options,” in ENUMATH 2011 Proceedings, 2012.
    7. B. Haasdonk, J. Salomon, and B. Wohlmuth, “A Reduced Basis Method for Parametrized Variational Inequalities,” University of Stuttgart, 2012.
    8. 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.
    9. 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.
    10. D. Wirtz and B. Haasdonk, “An Improved Vectorial Kernel Orthogonal Greedy Algorithm,” University of Stuttgart, 2012.
    11. D. Wirtz, D. C. Sorensen, and B. Haasdonk, “A-posteriori error estimation for DEIM reduced nonlinear dynamical  systems,” University of Stuttgart, 2012.
    12. D. Wirtz, N. Karajan, and B. Haasdonk, “Model order reduction of multiscale models using kernel methods,” SRC SimTech, University of Stuttgart, Germany, 2012.
    13. 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.
    14. 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.
  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. M. Drohmann, B. Haasdonk, and M. Ohlberger, “Adaptive Reduced Basis Methods for Nonlinear Convection-Diffusion  Equations,” in In Proc. FVCA6, 2011.
    3. 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.
    4. B. Haasdonk, “Reduzierte-Basis-Methoden, Vorlesungsskript SS 2011,” University of Stuttgart, 2011–004, 2011.
    5. 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.
    6. B. Haasdonk and M. Ohlberger, “Efficient reduced models and ıt 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. 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.
    8. 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.
  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. B. Haasdonk, M. Ohlberger, T. Tonn, and K. Urban, MoRePaS 2009 Book of Abstracts. University of Münster, 2009.
    2. B. Haasdonk and M. Ohlberger, “Efficient a-posteriori Error Estimation for Parametrized Reduced  Dynamical Systems,” in GMA-Fachaussschuss 1.30, Tagungsband, 2009.
    3. 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.
    4. 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.
    5. 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.
    6. N. Jung, B. Haasdonk, and D. Kröner, “Reduced Basis Method for Quadratically Nonlinear Transport Equations,” IJCSM, vol. 2, no. 4, pp. 334–353, 2009.
    7. 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.
  11. 2008

    1. 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.
    2. 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.
    3. B. Haasdonk and E. Pekalska, “Indefinite Kernel Fisher Discriminant,” in Proc. ICPR 2008, International Conference on Pattern Recognition, 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. 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.
    6. 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.
    7. E. Pekalska and B. Haasdonk, “Kernel Quadratic Discriminant Analysis with Positive and Indefinite  Kernels,” University of Münster, 06/08, 2008.
  12. 2007

    1. 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.
    2. 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.
    3. 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.
    4. B. Haasdonk and H. Burkhardt, “Invariant Kernels for Pattern Analysis and Machine Learning,” Machine Learning, vol. 68, pp. 35--61, 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. H. Burkhardt and B. Haasdonk, “Mustererkennung WS 02/03, ein multimedialer Grundlagenkurs im  Hauptstudium Informatik.” 2003.
    2. B. Haasdonk, B. R. Poluru, and A. Teynor, “Presto-Box 1.1 Library Documentation,” IIF-LMB, Universität Freiburg, 2/03, 2003.
    3. 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.
  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.

Contact

Dieses Bild zeigt Haasdonk
Prof. Dr.

Bernard Haasdonk

Head of Group, Dean of Studies (Studiendekan Lehramt)