Note
This page is currently under construction and extremely incomplete, until the coupling to the PUMA bibliography system is realized. Please refer to the publication list of Prof. Dr. B. Haasdonk until then.
Publications
2019
- M. Köppel et al., “Comparison of data-driven uncertainty quantification methods for a carbon dioxide storage benchmark scenario,” Comput. Geosci., vol. 2, no. 23, Art. no. 23, 2019, doi: https://doi.org/10.1007/s10596-018-9785-x.
2018
- B. M. Afkham, A. Bhatt, B. Haasdonk, and J. S. Hesthaven, “Symplectic Model-Reduction with a Weighted Inner Product,” 2018.
- T. Brünnette, G. Santin, and B. Haasdonk, “Greedy kernel methods for accelerating implicit integrators for parametric ODEs,” 2018, vol. Proceedings of ENUMATH 2017, [Online]. Available: http://www.simtech.uni-stuttgart.de/publikationen/prints.php?ID=1767.
- 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, Art. no. 1, 2018, doi: doi:10.3390/mca23010008.
- 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.
- 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, Art. no. ja, 2018, doi: 10.1002/cnm.3095.
- D. Wittwar and B. Haasdonk, “Greedy Algorithms for Matrix-Valued Kernels,” University of Stuttgart, 2018. [Online]. Available: http://www.simtech.uni-stuttgart.de/publikationen/prints.php?ID=1773.
2017
- 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.
- 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, [Online]. Available: http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=INPROC-2017-02&engl=1.
- 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.
- 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, doi: 10.1007/s10915-017-0430-y.
- 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, [Online]. Available: http://www.emis.de/journals/DRNA/9-2.html.
- A. Schmidt and B. Haasdonk, “Data-driven surrogates of value functions and applications to feedback control for dynamical systems,” University of Stuttgart, 2017. [Online]. Available: http://www.simtech.uni-stuttgart.de/publikationen/prints.php?ID=1742.
- A. Schmidt and B. Haasdonk, “Reduced basis approximation of large scale parametric algebraic Riccati equations,” ESAIM: Control, Optimisation and Calculus of Variations, 2017, doi: 10.1051/cocv/2017011.
- 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.
- D. Wittwar, A. Schmidt, and B. Haasdonk, “Reduced Basis Approximation for the Discrete-time Parametric Algebraic Riccati Equation,” University of Stuttgart, 2017.
2016
- 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, Art. no. 6, 2016, doi: 10.1186/s40323-016-0059-7.
- A. C. Antoulas, B. Haasdonk, and B. Peherstorfer, MORML 2016 Book of Abstracts. University of Stuttgart, 2016.
- 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.
- K. Carlberg, L. Brencher, B. Haasdonk, and A. Barth, “Data-driven time parallelism via forecasting,” 2016.
- 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, Art. no. 3, 2016, doi: 10.1051/cocv/2015019.
- D. Garmatter, B. Haasdonk, and B. Harrach, “A reduced Landweber Method for Nonlinear Inverse Problems,” Inverse Problems, vol. 32, no. 3, Art. no. 3, 2016, doi: http://dx.doi.org/10.1088/0266-5611/32/3/035001.
- M. Redeker and B. Haasdonk, “A POD-EIM reduced two-scale model for precipitation in porous media,” MATHEMATICAL AND COMPUTER MODELLING OF DYNAMICAL SYSTEMS, vol. 20, no. 4, Art. no. 4, 2016, doi: 10.1080/13873954.2016.1198384.
- A. Schmidt and B. Haasdonk, “Reduced basis method for H2 optimal feedback control problems,” IFAC-PapersOnLine, vol. 49, no. 8, Art. no. 8, 2016, doi: http://dx.doi.org/10.1016/j.ifacol.2016.07.462.
2015
- D. Amsallem, C. Farhat, and B. Haasdonk, “Special Issue on Model Reduction,” IJNME, International Journal of Numerical Methods in Engineering, vol. 102, no. 5, Art. no. 5, 2015, doi: 10.1002/nme.4889.
- M. Dihlmann and B. Haasdonk, “A reduced basis Kalman filter for parametrized partial differential equations,” ESAIM: Control, Optimisation and Calculus of Variations, 2015, doi: 10.1051/cocv/2015019.
- M. A. Dihlmann and B. Haasdonk, “Certified PDE-constrained parameter optimization using reduced basis surrogate models for evolution problems,” COMPUTATIONAL OPTIMIZATION AND APPLICATIONS, vol. 60, no. 3, Art. no. 3, 2015, doi: 10.1007/s10589-014-9697-1.
- 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,” INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING, vol. 102, no. 5, SI, Art. no. 5, SI, 2015, doi: 10.1002/nme.4773.
- 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, doi: 10.1007/978-3-319-10705-9_43.
- 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, Art. no. 5, 2015, doi: 10.1007/s10444-014-9396-6.
- M. Redeker and B. Haasdonk, “A POD-EIM reduced two-scale model for crystal growth,” Advances in Computational Mathematics, vol. 41, no. 5, Art. no. 5, 2015, doi: 10.1007/s10444-014-9367-y.
- 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, doi: 10.1016/j.ifacol.2015.05.016.
- 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, Art. no. 1, 2015, doi: 10.1002/nme.4767.
- 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, Art. no. 1, 2015, doi: 10.1002/nme.4767.
2014
- 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, vol. 6, pp. 685--712, 2014, doi: 10.1137/140981216.
- B. Haasdonk, “Reduced Basis Methods for Parametrized PDEs -- A Tutorial Introduction for Stationary and Instationary Problems,” IANS, University of Stuttgart, Germany, {S}im{T}ech Preprint, 2014. [Online]. Available: http://www.simtech.uni-stuttgart.de/publikationen/prints.php?ID=938.
- B. Haasdonk and M. Ohlberger, “Wenn die Probleme zahlreicher werden: Reduzierte Basis Methoden f�r effiziente und gesicherte numerische Simulation,” GAMM Rundbrief, vol. 2014, no. 1, Art. no. 1, 2014.
- 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, [Online]. Available: http://arxiv.org/abs/1405.2810v1.
- 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, doi: 10.1016/j.apnum.2013.12.001.
- D. Wirtz, D. C. Sorensen, and B. Haasdonk, “A Posteriori Error Estimation for DEIM Reduced Nonlinear Dynamical Systems,” SIAM Journal on Scientific Computing, vol. 36, no. 2, Art. no. 2, 2014, doi: 10.1137/120899042.
2013
- D. Amsallem, B. Haasdonk, and G. Rozza, “A Conference within a Conference for MOR Researchers,” SIAM News, vol. 46, no. 6, Art. no. 6, 2013, [Online]. Available: http://www.siam.org/news/news.php?id=2089.
- 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. Cvetkovic, T. Atanackovic and V. Kostic, vol. 13, no. 1, Art. no. 1, 2013, doi: doi: 10.1002/pamm.201310002.
- 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), SimTech Preprint, 2013.
- 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, Art. no. 8, 2013, doi: 10.1002/zamm.201200014.
- 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.
- B. Haasdonk, “Convergence Rates of the POD--Greedy Method,” ESAIM: Mathematical Modelling and Numerical Analysis, vol. 47, no. 3, Art. no. 3, 2013, doi: 10.1051/m2an/2012045.
- 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, [Online]. Available: http://www.lacan.upc.edu/admos2013/Proceedings.html.
- D. Wirtz and B. Haasdonk, “An Improved Vectorial Kernel Orthogonal Greedy Algorithm,” Dolomites Research Notes on Approximation, vol. 6, pp. 83–100, 2013, [Online]. Available: http://drna.di.univr.it/papers/2013/WirtzHaasdonk.2013.VKO.pdf.
- D. Wirtz and B. Haasdonk, “A Vectorial Kernel Orthogonal Greedy Algorithm,” Dolomites Res. Notes Approx., vol. 6, pp. 83–100, 2013, [Online]. Available: http://drna.padovauniversitypress.it/system/files/papers/WirtzHaasdonk-2013-VKO.pdf.
2012
- F. Albrecht, B. Haasdonk, S. Kaulmann, and M. Ohlberger, “The Localized Reduced Basis Multiscale Method,” in Algoritmy 2012, 2012, pp. 393--403, [Online]. Available: http://www.iam.fmph.uniba.sk/algoritmy2012/.
- M. Dihlmann, S. Kaulmann, and B. Haasdonk, “Online Reduced Basis Construction Procedure for Model Reduction of Parametrized Evolution Systems,” 2012.
- M. Drohmann, B. Haasdonk, and M. Ohlberger, “Reduced Basis Model Reduction of Parametrized Two-phase Flow in Porous Media,” 2012, doi: https://doi.org/10.3182/20120215-3-AT-3016.00128.
- 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, Art. no. 2, 2012, doi: 10.1137/10081157X.
- 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.
- B. Haasdonk, J. Salomon, and B. Wohlmuth, “A Reduced Basis Method for the Simulation of American Options,” 2012, [Online]. Available: http://arxiv.org/pdf/1201.3289v1.
- B. Haasdonk, J. Salomon, and B. Wohlmuth, “A Reduced Basis Method for Parametrized Variational Inequalities,” University of Stuttgart, {S}im{T}ech Preprint, 2012.
- 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.
- 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, [Online]. Available: http://www.biomedcentral.com/1752-0509/6/81.
- D. Wirtz and B. Haasdonk, “An Improved Vectorial Kernel Orthogonal Greedy Algorithm,” University of Stuttgart, SimTech Preprint, 2012. [Online]. Available: http://www.simtech.uni-stuttgart.de/publikationen/prints.php?ID=742.
- D. Wirtz, D. C. Sorensen, and B. Haasdonk, “A-posteriori error estimation for DEIM reduced nonlinear dynamical systems,” University of Stuttgart, SimTech Preprint, 2012. [Online]. Available: http://www.simtech.uni-stuttgart.de/publikationen/prints.php?ID=733.
- D. Wirtz, N. Karajan, and B. Haasdonk, “Model order reduction of multiscale models using kernel methods,” SRC SimTech, University of Stuttgart, Germany, Preprint, 2012.
- D. Wirtz and B. Haasdonk, “Efficient a-posteriori error estimation for nonlinear kernel-based reduced systems,” Systems and Control Letters, vol. 61, no. 1, Art. no. 1, 2012, doi: 10.1016/j.sysconle.2011.10.012.
- D. Wirtz and B. Haasdonk, “A-posteriori error estimation for parameterized kernel-based systems,” 2012, [Online]. Available: http://www.ifac-papersonline.net/.
2011
- M. Dihlmann, M. Drohmann, and B. Haasdonk, “Model Reduction of Parametrized Evolution Problems using the Reduced basis Method with Adaptive Time-Partitioning,” 2011.
- M. Drohmann, B. Haasdonk, and M. Ohlberger, “Adaptive Reduced Basis Methods for Nonlinear Convection-Diffusion Equations,” 2011.
- 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.
- B. Haasdonk, “Reduzierte-Basis-Methoden, Vorlesungsskript SS 2011,” University of Stuttgart, IANS-Report 2011–004, 2011.
- 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, Art. no. 4, 2011, doi: 10.1080/13873954.2011.547661.
- 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, Art. no. 2, 2011, doi: 10.1080/13873954.2010.514703.
- 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, Art. no. 4, 2011, doi: 10.1080/13873954.2011.582120.
- 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, Art. no. 23–24, 2011, doi: 10.1016/j.crma.2011.10.024.
2010
- B. Haasdonk, “Effiziente und Gesicherte Modellreduktion f�r Parametrisierte Dynamische Systeme.,” at - Automatisierungstechnik, vol. 58, no. 8, Art. no. 8, 2010.
- E. Pekalska and B. Haasdonk, “Indefinite Kernel Discriminant Analysis,” 2010.
2009
- B. Haasdonk, M. Ohlberger, T. Tonn, and K. Urban, MoRePaS 2009 Book of Abstracts. University of M�nster, 2009.
- B. Haasdonk and M. Ohlberger, “Efficient a-posteriori Error Estimation for Parametrized Reduced Dynamical Systems,” 2009.
- B. Haasdonk and M. Ohlberger, “Space-Adaptive Reduced Basis Simulation for Time-Dependent Problems,” 2009, [Online]. Available: http://www.ians.uni-stuttgart.de/am/Haasdonk/publications/mathmod2009_Nadapt.pdf.
- B. Haasdonk and M. Ohlberger, “Efficient Reduced Models for Parametrized Dynamical Systems by Offline/Online Decomposition,” 2009, [Online]. Available: http://www.ians.uni-stuttgart.de/am/Haasdonk/publications/mathmod2009_PMOR.pdf.
- 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.
- N. Jung, B. Haasdonk, and D. Kröner, “Reduced Basis Method for Quadratically Nonlinear Transport Equations,” IJCSM, vol. 2, no. 4, Art. no. 4, 2009.
- 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, Art. no. 6, 2009.
2008
- 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, [Online]. Available: http://pc2.iam.fmph.uniba.sk/amuc/_contributed/algo2009/drohmann.pdf.
- 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.
- B. Haasdonk and E. Pekalska, “Indefinite Kernel Fisher Discriminant,” 2008.
- B. Haasdonk and M. Ohlberger, “Reduced basis method for finite volume approximations of parametrized linear evolution equations,” ESAIM: M2AN, vol. 42, no. 2, Art. no. 2, 2008, doi: 10.1051/m2an:2008001.
- B. Haasdonk and E. Pekalska, “Classification with Kernel Mahalanobis Distances,” 2008.
- 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, [Online]. Available: http://etna.mcs.kent.edu/vol.32.2008/pp145-161.dir/pp145-161.pdf.
- E. Pekalska and B. Haasdonk, “Kernel Quadratic Discriminant Analysis with Positive and Indefinite Kernels,” University of Münster, Preprint Angewandte Mathematik und Informatik 06/08, 2008.
2007
- 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.
- 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.
- 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.
- B. Haasdonk and H. Burkhardt, “Invariant Kernels for Pattern Analysis and Machine Learning,” Machine Learning, vol. 68, pp. 35--61, 2007, doi: DOI 10.1007/s10994-007-5009-7.
- B. Haasdonk and H. Burkhardt, “Classification with Invariant Distance Substitution Kernels,” 2007.
2006
- 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.
- 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.
2005
- B. Haasdonk, “Transformation Knowledge in Pattern Analysis with Kernel Methods, Distance and Integration Kernels,” Albert-Ludwigs-Universität, Freiburg im Breisgau, Fakultät für Angewandte Wissenschaften, 2005.
- B. Haasdonk, “Feature Space Interpretation of SVMs with Indefinite Kernels,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 4, Art. no. 4, 2005, doi: http://doi.ieeecomputersociety.org/10.1109/TPAMI.2005.78.
- B. Haasdonk, A. Vossen, and H. Burkhardt, “Invariance in Kernel Methods by Haar-Integration Kernels,” 2005.
2004
- B. Haasdonk and C. Bahlmann, “Learning with Distance Substitution Kernels,” in Pattern Recognition - Proceedings of the 26th DAGM Symposium, 2004, pp. 220–227.
- 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, doi: http://dx.doi.org/10.1109/ICPR.2004.1334372.
2003
- H. Burkhardt and B. Haasdonk, “Mustererkennung WS 02/03, ein multimedialer Grundlagenkurs im Hauptstudium Informatik.” 2003.
- B. Haasdonk, B. R. Poluru, and A. Teynor, “Presto-Box 1.1 Library Documentation,” IIF-LMB, Universit�t Freiburg, 2/03, 2003.
- 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, Art. no. 3, 2003, doi: 10.1007/s00607-003-1476-2.
2002
- 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.
- 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.
2001
- 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, Art. no. 3, 2001, doi: 10.1007/s211-001-8011-x.
- 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.
- 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, Art. no. 3, 2001, doi: 10.1007/s211-001-8011-x.
2000
- 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.
1999
- Ge et al., “A Procedural Interface for Multiresolutional Visualization of General Numerical Data,” University of Bonn, SFB 256 Report 28, 1999.
- B. Haasdonk, “Konvergenz eines Staggered Lax-Friedrichs Verfahrens auf unstrukturierten 2D Gittern,” 1999.
Contact

Prof. Dr.
Head of Group Numerical Mathematics