Bernard Haasdonk

The following is a complete list of my publications. For many of the entries I provide links to preprints. You may find bibitems, URLs or further electronic versions in my google-scholar profile

2024

Ehring, T., Haasdonk, B.: Online adaptive surrogates for the value function of high-dimensional nonlinear optimal control problems. Submitted, 2024.

Santin, G., Wenzel, T., Haasdonk, B.: On the optimality of target-data-dependent kernel greedy interpolation in Sobolev Reproducing Kernel Hilbert Spaces. SIAM Journal on Numerical Analysis, 62(5), 2024. https://doi.org/10.1137/23M1587956, preprint arXiv:2307.09811

Rettberg, J., Kneifl, J., Herb, J., Buchfink, P., Fehr, J., Haasdonk, B.: Data-driven identification of latent port-Hamiltonian systems. preprint arXiv:2408.08185, 2024. Software: https://doi.org/10.18419/darus-4446, Disc brake data https://doi.org/10.18419/darus-4418

Rettberg, J., Wittwar, D., Buchfink, P., Herkert, R., Fehr, J., Haasdonk, B.: Improved a posteriori error bounds for reduced port-Hamiltonian systems. ACOM, 50:100, 2024. https://doi.org/10.1007/s10444-024-10195-8, View-Only Link, preprint arXiv:2303.17329

Buchfink, P., Glas, S., Haasdonk, B, Unger, B.: Model reduction on manifolds: a differential geometric framework. Physica D, 468:134299, 2024. https://doi.org/10.1016/j.physd.2024.134299. preprint arXiv:2312.01963

Wenzel, T., Winkle, D., Santin, G. and Haasdonk, B.: Adaptive meshfree approximation for linear elliptic partial differential equations with PDE-greedy kernel methods. preprint arXiv:2207.13971v2, 2024.

Wenzel, T., Haasdonk, B., Kleikamp, H., Ohlberger, M., Schindler, F.: Application of Deep Kernel Models for Certified and Adaptive RB-ML-ROM Surrogate Modeling. In Proc. LSSC 2023, Springer LNCS 13952:117-125, 2024. https://doi.org/10.1007/978-3-031-56208-2_11. preprint arXiv:2302.14526

Herkert, R., Buchfink, P., Wenzel, T., Haasdonk, B., Toktaliev, P., Iliev, O.: Greedy Kernel Methods for Approximating Breakthrough Curves for Reactive Flow from 3D Porous Geometry Data. Mathematics, 2024. https://doi.org/10.3390/math12132111, code data repository https://doi.org/10.18419/darus-4227. preprint arxiv:2405.19170.

Herkert, R, Buchfink, P., Haasdonk, B., Rettberg, J., Fehr, J.: Error Analysis of Randomized Symplectic Model Order Reduction for Hamiltonian Systems, 2024. preprint arxiv:2405.10465. Code data repository https://doi.org/10.18419/darus-4185. 

Herkert, R., Buchfink, P., Haasdonk, B., Rettberg, H., Fehr, J.: Randomized Symplectic Model Order Reduction for Hamiltonian Systems. In Proc. LSSC 2023, Springer LNCS 13952:99-107, 2024. https://doi.org/10.1007/978-3-031-56208-2_9. preprint arXiv:2303.04036.

Wenzel, T., Santin, G., Haasdonk, B.: Stability of convergence rates: Kernel interpolation on non-Lipschitz domains. IMA Journal of Numerical Analysis, 44(3):1-22, 2024. https://doi.org/10.1093/imanum/drae014. preprint arXiv:2203.12532.

Fokina, D., Toktaliev, P., Herkert, R., Wenzel, T., Haasdonk, B., Iliev, O.: Machine learning methods based prediction of
breakthrough curves in reactive porous media from Peclet and Damköhler numbers. In Proc. 18th. BGSIAM, Springer, 2024. to appear.

Ehring, T., Haasdonk, B.: Hermite kernel surrogates for the value function of high-dimensional nonlinear optimal control problems. ACOM, 2024. https://doi.org/10.1007/s10444-024-10128-5, preprint arXiv:2305.06122.

Buchfink, P., Glas, S., Haasdonk, B.: Approximation Bounds for Model Reduction on Polynomially Mapped Manifolds. Comptes Rendus, Série Mathématique, 2024. Accepted, preprint arXiv:2312.00724

Herkert, R., Buchfink, P., Haasdonk, B.: Dictionary-based Online-adaptive Structure-preserving Model Order Reduction for Parametric Hamiltonian Systems. ACOM, 50(12), 2024, https://doi.org/10.1007/s10444-023-10102-7. preprint arXiv:2303.18072

Döppel, F., Wenzel, T., Herkert, R., Haasdonk, B., Votsmeier, M.: Goal-Oriented Two-Layered Kernel Models as Automated Surrogates for Surface Kinetics in Reactor Simulations. Chemie Ingenieur Technik, 2024. http://doi.org/10.1002/cite.202300178

Hammer, M., Wenzel, T., Santin, G., Meszaros-Beller, L., Little, J.P., Haasdonk, B., Schmitt, S.: A new method to design energy-conserving surrogate models for the coupled, non-linear responses of intervertebral discs. Biomechanics and
Modeling in Mechanobiology, 2024.https://doi.org/10.1007/s10237-023-01804-4, ViewOnly URL

2023

Yeh, Y.-C., Ebbing, T., Frick, K., Schmid-Staiger, U., Haasdonk, B., Tovar, G. E. M.: Improving Determination of Pigment Contents in Microalgae Suspension with Absorption Spectroscopy: Light Scattering Effect and Bouguer–Lambert–Beer Law, Mar. Drugs, 21(12), 619, 2023. https://doi.org/10.1016/10.3390/md21120619

Yeh, Y.-C., Syed, T., Brinitzer, G., Frick, K., Schmid-Staiger, U., Haasdonk, B., Tovar, G.E.M., Krujatz, F., Mädler, J.,  Urbas, L.: Improving Microalgae Growth Modeling with Light History Data using Machine Learning Models: A Comparative Study. Bioresource Technology, 390: 129882, 2023. https://doi.org/10.1016/j.biortech.2023.129882

Rettberg, J., Wittwar, D., Buchfink, P., Brauchler, A., Ziegler, P., Fehr, J., Haasdonk, B.: Port-Hamiltonian Fluid-Structure Interaction Modeling and Structure-Preserving Model Order Reduction of a Classical Guitar. MCMDS, Mathematical and Computer Modelling of Dynamical Sytems, 29(1):116-148, 2023. https://doi.org/10.1080/13873954.2023.2173238, arxiv: 2203.10061. Software CCMOR2 available at https://doi.org/10.18419/darus-3839

Haasdonk, B., Kleikamp, H., Ohlberger, M., Schindler, F., Wenzel, T.: A new certified hierarchical and adaptive RB-ML-ROM surrogate model for parametrized PDEs. SIAM Journal on Scientific Computing, 45(3):A1039-S460, 2023. https://doi.org/10.1137/22M1493318, preprint arXiv:2204.13454

Buchfink, P., Glas, S., Haasdonk, B.: Symplectic Model Reduction of Hamiltonian Systems on Nonlinear Manifolds, SIAM Journal on Scientific Computing, 45(2), 2023. https://epubs.siam.org/doi/full/10.1137/21M1466657, preprint arXiv:2112.10815, correspondng software: https://github.com/pbuchfink/manifold-mor-wave

Yeh, Y.-C., Haasdonk, B., Schmid-Staiger, U., Stier, M. and Tovar, G.E.M.: A novel model extended from the Bouguer-Lambert-Beer law can describe the nonlinear absorbance of potassium dichromate solutions and microalgae suspensions. Frontiers in Bioengineering and Biotechnology, Vol 11, 2023. https://doi.org/10.3389/fbioe.2023.1116735 

2022

Wenzel, T., Santin, G. and Haasdonk, B.: Analysis of target data-dependent greedy kernel algorithms: Convergence rates for f -, f · P - and f /P -greedy. Constructive Approximation, 57, 45-74, 2023. https://doi.org/10.1007/s00365-022-09592-3, preprint arXiv:2105.07411

Ehring, T., Haasdonk, B.: Greedy sampling and approximation for realizing feedback control for high dimensional nonlinear systems, 2022, In Proc. MATHMOD 2022, IFAC Papers Online, 55(20):325-330, 2022. https://doi.org/10.1016/j.ifacol.2022.09.116, https://www.sciencedirect.com/science/article/pii/S2405896322013106

Buchfink, P., Glas, S., Haasdonk, B.: Optimal Bases for Symplectic Model Order Reduction of Canonizable Linear Hamiltonian Systems. In Proc. MATHMOD 2022, IFAC Papers Online, 55(20),463-468, 2022. https://doi.org/10.1016/j.ifacol.2022.09.138, https://www.sciencedirect.com/science/article/pii/S2405896322013398  

Santin, G.; Karvonen, T.; Haasdonk, B.: Sampling based approximation of linear functionals in Reproducing Kernel Hilbert Spaces, BIT Numerical Mathematics, 62, 279-310, 2022. https://doi.org/10.1007/s10543-021-00870-3, preprint arXiv:2004.00556

2021

Santin, G. and Haasdonk, B.: Kernel Methods for Surrogate Modelling, Chapter in P. Benner, W. Schilders, S. Grivet-Talocia, Q. Quarteroni, G. Rozza and L. M. Silveira (Eds.) Model Order Reduction, Volume 1, System- and Data-Driven Methods and Algorithms, de Gruyter, 2021, ISBN 978-3-11-050043-1, pages 311--353. https://doi.org/10.1515/9783110498967-009, preprint arXiv:1907.10556

Haasdonk, B, Ohlberger, M., Schindler F.: An adaptive model hierarchy for data-augmented training of kernel models for reactive flow, 2021. MATHMOD 2022 Discussion Contribution Volume, ARGESIM Report 17, p 67-68, 2022, https://doi.org/10.11128/arep.17.a17155,  preprint arXiv:2110.12388

Leiteritz, R., Buchfink, P., Pflüger, D., Haasdonk, B.: Surrogate Data Enriched Physics-Aware Neural Networks, In Proc. NLDL 2022, 2022. https://doi.org/10.7557/18.6268, preprint arxiv:2112.05489

Ehring, T., Haasdonk, B.: Feedback control for a coupled soft tissue system by kernel surrogates, In Proc. COUPLED, Scipedia, 2021. https://dx.doi.org/10.23967/coupled.2021.026

Haasdonk, B., Hamzi, B., Santin, G. and Wittwar, D.: Kernel methods for center manifold approximation and a weak data-based version of the Center Manifold Theorem. Physica D: Nonlinear Phenomena, vol. 427, 28 pages, 2021. https://dx.doi.org/10.1016/j.physd.2021.133007, preprint arXiv:2012.0038,

Wenzel, T., Santin, G. and Haasdonk, B. Universality and Optimality of Structured
Deep Kernel Networks. University of Stuttgart, 2021. Submitted. preprint arXiv:2105.07228

Gavrilenko, P., Haasdonk, B, Iliev, O., Ohlberger, M., Schindler, F., Toktaliev, P., Wenzel, T., Youssef, M.: A full order, reduced order and machine learning model pipeline for efficient prediction of reactive flows. In Proc. LSSC'21, Springer Lecture Notes in Computer Science, Volume 13127, pp. 378-386, 2021. https://doi.org/10.1007/978-3-030-97549-4_43, preprint arXiv:2104.02800

Wenzel, T., Kurz, M., Beck, A., Santin, G., Haasdonk, B.: Structured Deep Kernel Networks for Data-driven Closure Terms of Turbulent Flows. In Proc. LSSC'21, Springer Lecture Notes in Computer Science, Volume 13127, pp. 410-418, 2021. https://dx.doi.org/10.1007/978-3-030-97549-4_47, preprint arXiv:2103.13655

Shuva, S., Buchfink, P., Röhrle, O., Haasdonk, B.: Reduced Basis Methods for Efficient Simulation of a Rigid Robot Hand Interacting with Soft Tissue. In Proc. LSSC'21, Springer Lecture Notes in Computer Science, Volume 13127, pp.402-409, 2021. https://doi.org/10.1007/978-3-030-97549-4_46, arXiv:2103.15422

Wenzel, T., Santin, G., Haasdonk, B.:  A novel class of stabilized greedy kernel approximation algorithms: Convergence, stability & uniform point distribution, Journal of Approximation Theory, 262:105508, 2021. https://dx.doi.org/10.1016/j.jat.2020.105508, preprint arXiv 1911.04352

Haasdonk, B: MOR Software, Chapter in P. Benner, W. Schilders, S. Grivet-Talocia, Q. Quarteroni, G. Rozza and L. M. Silveira (Eds.): Model Order Reduction, Volume 3, Applications, de Gruyter, 2021. ISBN 9783110500448, pages 431-464. https://doi.org/10.1515/9783110499001-013,  (preprint)

Wittwar, D.; Haasdonk, B.: Convergence Rates for Matrix P-Greedy Variants. In F.J. Vermolen, C. Vuik (Eds): Numerical Mathematics and Advanced Applications ENUMATH 2019, pp. 1195-1203, Springer, 2021. https://doi.org/10.1007/978-3-030-55874-1_119

Buchfink, P.; Haasdonk, B.: Experimental Comparison of Symplectic and Non-Symplectic Model Order Reduction on an Uncertainty Quantification Problem. In F.J. Vermolen, C. Vuik (Eds): Numerical Mathematics and Advanced Applications ENUMATH 2019, pp. 205-214, Springer, 2021. https://dx.doi.org/10.1007/978-3-030-55874-1_19

Haasdonk, B.; Wenzel, T.; Santin, G.; Schmitt, S.: Biomechanical surrogate modelling using stabilized vectorial greedy kernel methods. In F.J. Vermolen, C. Vuik (Eds): Numerical Mathematics and Advanced Applications ENUMATH 2019, pp. 499-508, Springer, 2021. https://dx.doi.org/10.1007/978-3-030-55874-1_49

2020

Buchfink P., Haasdonk B., Rave S.: PSD-Greedy Basis Generation for Structure-Preserving Model Order Reduction of Hamiltonian Systems, In Proc. ALGORITMY, Vydavatel'stvo SPEKTRUM, 2020.
http://www.iam.fmph.uniba.sk/amuc/ojs/index.php/algoritmy/article/view/1577

Grunert, D., Fehr, J., Haasdonk, B.: Well-scaled, a posteriori error estimation for model order reduction of large second-order mechanical systems. ZAMM, 100(8), 2020. https://doi.org/10.1002/zamm.201900186

Schmidt, A; Wittwar, D.; Haasdonk, B.: Rigorous and effective a-posteriori error bounds for nonlinear problems - Application to RB methods. ACOM, Advances in Computational Mathematics, 46, Article Number 32, 2020. https://doi.org/10.1007/s10444-020-09741-x, preprint

Fehr, J., Haasdonk, B. (Eds.): Model Reduction of Coupled Systems – MORCOS 2018, IUTAM Symposia Proceedings Series, Springer, ISBN 978-3-030-21015-1, 2020.

Alla, A.; Haasdonk, B., Schmidt, A.: Feedback control of parametrized PDEs via model order reduction and dynamic programming principle, ACOM, Advances in Computational Mathematics, 46, Article Number 9, 2020. https://doi.org/10.1007/s10444-020-09744-8 (preprint arXiv 180.00021)

2019

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, 23:339–354, 2019. https://doi.org/10.1007/s10596-018-9785-x, preprint arXiv 1802.03064

Denzel, A., Haasdonk, B., Kästner, J.: Gaussian Process Regression for Minimum Energy Path Optimization and Transition State Search, The Journal of Physical Chemistry A, 123(44):9600–9611, 2019. https://doi.org/10.1021/acs.jpca.9b08239

Carlberg, K., Brencher, L., Haasdonk, B., Barth, A.: Data-driven time parallelism via forecasting, SISC, SIAM Journal on Scientific Computing, 41(3), B466–B496, 2019. https://dx.doi.org/10.1137/18M1174362, 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

Hamzi, B., Haasdonk, B., Santin, G., Wittwar, D.: Greedy Kernel Methods for Center Manifold Approximation, Proc. ICOSAHOM 2018, pp. 95-106, Springer, 2019. https://doi.org/10.1007/978-3-030-39647-3_6, 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, pp 95-110, Springer, 2019. https://doi.org/10.1007/978-3-030-21013-7_7

Föll, R., Haasdonk, B., Hanselmann, M., Ulmer, H.: Deep recurrent Gaussian process with variational Sparse Spectrum approximation, 2019, preprint arXiv:1909.13743.

Bhatt, A., Fehr, J., Haasdonk, B.: Model Order Reduction of an Elastic Body under Large Rigid Motion, In Proc. ENUMATH 2017, Voss, Norway, pages 269--277, Springer, 2019. https://doi.org/10.1007/978-3-319-96415-7_23

Brünnette, T., Santin, G., Haasdonk, B.: Greedy kernel methods for accelerating implicit integrators for parametric ODEs, In Proc. Proc. ENUMATH 2017, Voss, Norway, pp. 889–896, Springer, 2019. https://dx.doi.org/10.1007/978-3-319-96415-7_84, preprint arXiv:1802.08106

Wittwar, D., Haasdonk, B.: Greedy Algorithms for Matrix-Valued Kernels, In Proc. ENUMATH 2017, Voss, Norway, pp. 113-121, Springer, 2019. https://dx.doi.org/10.1007/978-3-319-96415-7_8, preprint

2018

Schmidt, A., Haasdonk, B.: Reduced basis approximation of large scale parametric algebraic Riccati equations, ESAIM COCV: Control, Optimisation and Calculus of Variations, 24(1):129-151, 2018. https://dx.doi.org/10.1051/cocv/2017011

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(1), 8, 2018. https://doi.org/10.3390/mca23010008, preprint arXiv:1610.05029

Dibak, C., Haasdonk, B., Schmidt, A., Dürr, F. and Rothermel, K.: Enabling interactive mobile simulations through distributed reduced models, Pervasive and Mobile Computing, 45:19–34, 2018. https://doi.org/10.1016/j.pmcj.2018.02.002, preprint arXiv:1802.05206

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. https://dx.doi.org/10.1002/cnm.3095, preprint arXiv:1802.04628

Wittwar, D., Santin, G., Haasdonk, B.: Interpolation with uncoupled separable matrix-valued kernels, Dolomites Research Notes on Approximation, 11(3):23-39, 2018. https://dx.doi.org/10.14658/pupj-drna-2018-3-4, preprint arXiv:1807.09111

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

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

Fehr, J., Grunert, D., Bhatt, A., Haasdonk, B.: A Sensitivity Study of Error Estimation in Reduced Elastic Multibody Systems, In Proc. of MATHMOD 2018, Vienna, Austria, IFAC PapersOnLine, 51(2):202-207, 2018. https://doi.org/10.1016/j.ifacol.2018.03.035, preprint

Schmidt, A., Haasdonk, B.: Data-driven surrogates of value functions and applications to feedback control for dynamical systems, In Proc. of MATHMOD 2018, IFAC PapersOnLine, 51(2):307-312, Vienna, Austria, 2018. https://doi.org/10.1016/j.ifacol.2018.03.053

Martini, I.; Rozza, G., Haasdonk, B.: Certified Reduced Basis Approximation for the Coupling of Viscous and Inviscid Parametrized Flow Models, Journal of Scientific Computing, 74:197-219, 2018. https://doi.org/10.1007/s10915-017-0430-y

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, Mechanisms and Machine Science, 50:198–205, Springer International Publishing, 2018. https://doi.org/10.1007/978-3-319-60867-9_23, preprint

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. https://dx.doi.org/10.14658/pupj-drna-2017-Special_Issue-9, preprint arXiv 1612.02672

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

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, pp. 65-136, SIAM, Philadelphia, 2017. https://dx.doi.org/10.1137/1.9781611974829.ch2, preprint

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, pp. 377-407, SIAM, Philadelphia, 2017. https://doi.org/10.1137/1.9781611974829.ch9

Dibak, C., Schmidt, A., Dürr, 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), pp. 111-120, 2017. https://doi.org/10.1109/PERCOM.2017.7917857, (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, pp. 333-347, Springer, 2017. https://doi.org/10.1007/978-3-319-58786-8_21, preprint arXiv 1607.02337

Haasdonk, B., Santin, G.: Greedy Kernel Approximation for Sparse Surrogate Modelling, in Proc. KOMSO Workshop “Reduced-Order Modeling for Simulation and Optimization”, pp. 21-45, Springer, 2017. https://doi.org/10.1007/978-3-319-75319-5_2

Köppel, M., Franzelin, F., Kröker, I., Oladyshkin, S., Wittwar, D., Santin, G., Barth, A., Haasdonk, B., Nowak, W., Pflüger, D., & Rohde, C.: Datasets and executables of data-driven uncertainty quantification benchmark in carbon dioxide storage, 2017. https://doi.org/10.5281/zenodo.933827

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. https://dx.doi.org/10.1051/cocv/2015019

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. https://doi.org/10.1080/13873954.2016.1198384

Garmatter, D.; Haasdonk, B., Harrach, B.: A reduced Landweber Method for Nonlinear Inverse Problems, Inverse Problems, 32:1–21, 2016. https://dx.doi.org/10.1088/0266-5611/32/3/035001,preprint arXiv 1507.05434

Amsallem, D., Haasdonk, B.: PEBL-ROM: Projection-Error Based Local Reduced-Order Models, AMSES, Advanced Modeling and Simulation in Engineering Sciences, 3, 2016. https://doi.org/10.1186/s40323-016-0059-7

Schmidt, A., Haasdonk, B.: Reduced basis method for H2 optimal feedback control problems, In Proc. CPDE 2016, IFAC-PapersOnLine, 49(8):327–332, 2016. https://doi.org/10.1016/j.ifacol.2016.07.462

Schmidt, A., Dihlmann, M., Haasdonk, B.: Basis generation approaches for a reduced basis linear quadratic regulator. Proc. Mathmod 2015, IFAC-PapersOnLine, 48(1): 713-718, 2015. https://dx.doi.org/10.1016/j.ifacol.2015.05.016

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. https://dx.doi.org/10.1007/s10589-014-9697-1, preprint

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. https://dx.doi.org/10.1002/nme.4767

Redeker, M., Haasdonk, B.: A POD-EIM reduced two-scale model for crystal growth. ACOM, Advances in Computational Mathematics, 41:987–1013, 2015. https://doi.org/10.1007/s10444-014-9367-y , preprint

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. https://doi.org/10.1007/s10444-014-9396-6

Kaulmann, S., Flemisch, B., Lie, K.-A., Haasdonk, B., Ohlberger, M.: The Localized Reduced Basis Multiscale Method for two-phase flows in porous media. IJNME, Internat. J. Numer. Methods Engrg., 102:1018–1040, 2015. https://doi.org/10.1002/nme.4773, preprint arXiv:1405.2810

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. https://doi.org/10.1137/140981216, preprint arXiv:1408.1220

Martini, I., Haasdonk, B.: Output error bounds for the Dirichlet-Neumann reduced basis method. Proc. ENUMATH 2013, 103:437–445, Springer, 2015. https://doi.org/10.1007/978-3-319-10705-9_43

Amsallem, D.; Farhat, C., Haasdonk, B.: Editorial: Special Issue on Model Reduction, IJNME, International Journal of Numerical Methods in Engineering, 102, 931–932, 2015. https://doi.org/10.1002/nme.4889

2014

Maier, I., Haasdonk, B.: A Dirichlet-Neumann reduced basis method for homogeneous domain decomposition problems. Applied Numerical Mathematics, 78:31–48, 2014. https://doi.org/10.1016/j.apnum.2013.12.001, preprint

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. https://dx.doi.org/10.1137/120899042, electronic version (C) SIAM

Haasdonk, B., Ohlberger, M.: Wenn die Probleme zahlreicher werden: Reduzierte Basis Methoden für effiziente und gesicherte numerische Simulation. GAMM Rundbrief, 1:6–13, 2014. https://www.gamm.org/wp-content/uploads/2020/06/RB_2014_01_weba.pdf

2013

Haasdonk, B.: Convergence Rates of the POD-Greedy Method. M2AN Math. Model. Numer. Anal., 47(3):859–873, 2013. https://dx.doi.org/10.1051/m2an/2012045, preprint

Fehr, J., Fischer, M., Haasdonk, B., Eberhard, P.: Greedy based Approximation of Frequency-weighted Gramian Matrices for Model Reduction in Multibody Dynamics. ZAMM, 93(8):501–519, 2013. https://doi.org/10.1002/zamm.201200014

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(1):79–105, 2013. https://dx.doi.org/10.1137/120876745, preprint

Wirtz, D., Haasdonk, B.: A Vectorial Kernel Orthogonal Greedy Algorithm. Dolomites Research Notes on Approximation, 6:83–100, 2013. https://dx.doi.org/10.14658/PUPJ-DRNA-2013-Special_Issue-10,  preprint

Amsallem, D., Haasdonk, B., Rozza, G.: A Conference within a Conference for MOR Researchers. SIAM News, 46(6):8, 2013. https://sinews.siam.org/Portals/Sinews2/Issue%20Pdfs/sn_July-August2013.pdf

Kaulmann, S., Haasdonk, B.: Online Greedy Reduced Basis Construction using Dictionaries. Proc. ADMOS 2013, pp. 365-376, 2013. https://www.lacan.upc.edu/admos2013/proceedings/ADMOS_2013_EBOOK.pdf, preprint

Dihlmann, M., Haasdonk, B.: Certified Nonlinear Parameter Optimization with Reduced Basis Surrogate Models, Proc. Appl. Math. Mech., 13(1):3–6, 2013. https://dx.doi.org/10.1002/pamm.201310002

2012

Wirtz, D., Haasdonk, B.: Efficient a-posteriori error estimation for nonlinear kernel-based reduced systems. Systems & Control Letters, 61(1):203–211, 2012. https://dx.doi.org/10.1016/j.sysconle.2011.10.012,  preprint

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. https://dx.doi.org/10.1137/10081157X, preprint

Waldherr, S., Haasdonk, B.: Efficient Parametric Analysis of the Chemical Master Equation through Model Order Reduction. BMC Systems Biology, 6:81, 2012. https://doi.org/10.1186/1752-0509-6-81, preprint arXiv:1201.3749

Ruiner, T., Fehr, J., Haasdonk, B., Eberhard, P.: A-posteriori Error Estimators for Second Order Mechanical Systems. Acta Mechanica Sinica, 28(3):854–862, 2012. https://doi.org/10.1007/s10409-012-0114-7

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. https://doi.org/10.1137/110835372, preprint

Haasdonk, B., Salomon, J., Wohlmuth, B.: A Reduced Basis Method for the Simulation of American Options. In Proc. ENUMATH 2011, pp. 821-839, Springer, 2012. https://doi.org/10.1007/978-3-642-33134-3_85, preprint arXiv:1201.3289

Drohmann, M., Haasdonk, B., Ohlberger, M.: Reduced Basis Model Reduction of Parametrized Two-phase Flow in Porous Media. In Proc. of MATHMOD 2012, IFAC Proceedings Volumes, 45(2):722-727, 2012. https://dx.doi.org/10.3182/20120215-3-AT-3016.00128, preprint

Dihlmann, M., Kaulmann, S., Haasdonk, B.: Online Reduced Basis Construction Procedure for Model Reduction of Parametrized Evolution Systems. In Proc. of MATHMOD 2012, IFAC Proceedings Volumes, 45(2):112-117, 2012. https://doi.org/10.3182/20120215-3-AT-3016.00020,  preprint

Wirtz, D., Haasdonk, B.: A-Posteriori Error Estimation for Parametrized Kernel-based Systems. In Proc. of MATHMOD 2012, IFAC Proceedings Volumes, 45(2):763-768,2012. https://doi.org/10.3182/20120215-3-AT-3016.00135

Albrecht, F., Haasdonk, B., Kaulmann, S., Ohlberger, M.: The Localized Reduced Basis Multiscale Method. In Proc. ALGORITMY 2012, pp. 393-403, 2012. https://www.iam.fmph.uniba.sk/algoritmy2012/zbornik/40Albrecht.pdf, preprint

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. https://doi.org/10.1080/13873954.2010.514703, preprint

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. https://doi.org/10.1080/13873954.2011.547674, preprint

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. https://doi.org/10.1080/13873954.2011.582120

Kaulmann, S., Ohlberger, M., Haasdonk, B.: A New Local Reduced Basis Discontinuous Galerkin Approach for Heterogeneous Multiscale Problems. Comptes Rendus Mathematique, 349(23-24):1233–1238, 2011. https://doi.org/10.1016/j.crma.2011.10.024, preprint

Drohmann, M., Haasdonk, B., Kaulmann, S., Ohlberger, M.: A Software Framework for Reduced Basis Methods using Dune-RB and RBMatlab. In Proc. Dune User Meeting 2010, pp. 77-88, Springer, 2011. https://doi.org/10.1007/978-3-642-28589-9_6, preprint

Dihlmann, M., Drohmann, M., Haasdonk, B.: Model Reduction of Parametrized Evolution Problems using the Reduced basis Method with Adaptive Time-Partitioning. Proc. ADMOS 2011, pp. 156-167, 2011. https://congress.cimne.com/admos2011/frontal/doc/ADMOS2011.pdf, preprint

Drohmann, M., Ohlberger, M, Haasdonk, B.: Adaptive Reduced Basis Methods for Nonlinear Convection-Diffusion Equations. In Proc. FVCA 6, pp. 369-377, Springer, 2011. https://doi.org/10.1007/978-3-642-20671-9_39, preprint

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. https://doi.org/10.1080/13873954.2011.547661

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

2010

Haasdonk, B.: Effiziente und Gesicherte Modellreduktion für Parametrisierte Dynamische Systeme. at- Automatisierungstechnik, 58(8):468–474, 2010. https://doi.org/10.1524/auto.2010.0861, preprint

Haasdonk, B., Pekalska, E.: Indefinite Kernel Discriminant Analysis. In Proc. COMPSTAT 2010, pp. 221-230, Springer, 2010. https://doi.org/10.1007/978-3-7908-2604-3_20, preprint

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. https://doi.org/10.1109/TPAMI.2008.290, preprint

Jung, N., Haasdonk, B., Kröner D.: Reduced Basis Method for Quadratically Nonlinear Transport Equations. IJCSM, 2(4):334–353, 2009. https://doi.org/10.1504/IJCSM.2009.030912, preprint

Haasdonk, B., Ohlberger, M.: Reduced Basis Method for Explicit Finite Volume Approximations of Nonlinear Conservation Laws. In Proc. 12th International Conference on Hyperbolic Problems 2008: Theory, Numerics, Application, pp. 605-614,2009. https://doi.org/10.1090/psapm/067.2/2605256, preprint

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, pp. 111-120, 2009. https://www.iam.fmph.uniba.sk/amuc/_contributed/algo2009/drohmann.pdf, preprint

Haasdonk, B., Ohlberger, M.: Space-Adaptive Reduced Basis Simulation for Time-Dependent Problems. In Proc. MATHMOD 2009, 6th Vienna International Conference on Mathematical Modelling, 2009. https://doi.org/10.11128/arep.35, https://www.argesim.org/fileadmin/user_upload_argesim/ARGESIM_Publications_OA/MATHMOD_Publications_OA/MATHMOD_2009_AR34_35/full_papers/184.pdf, preprint

Haasdonk, B., Ohlberger, M.: Efficient Reduced Models for Parametrized Dynamical Systems by Offline/Online Decomposition. In Proc. MATHMOD 2009, 6th Vienna International Conference on Mathematical Modelling, 2009. https://doi.org/10.11128/arep.35, https://www.argesim.org/fileadmin/user_upload_argesim/ARGESIM_Publications_OA/MATHMOD_Publications_OA/MATHMOD_2009_AR34_35/full_papers/483.pdf, preprint

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, ISBN 978-3-9502451-7-2, TU Wien, 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. https://dx.doi.org/10.1051/m2an:2008001, preprint

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. https://etna.math.kent.edu/vol.32.2008/pp145-161.dir/pp145-161.pdf, preprint

Haasdonk, B., Ohlberger, M.: Adaptive Basis Enrichment for the Reduced Basis Method Applied to Finite Volume Schemes. In Proc. FVCA5, 5th International Symposium on Finite Volumes for Complex Applications, pp. 471-478, ISBN 9781848210356, Wiley, 2008. preprint

Haasdonk, B., Pekalska, E.: Classification with Kernel Mahalanobis Distance Classifiers. In Proc. of 32nd. GfKl Conference, Advances in Data Analysis, Data Handling and Business Intelligence, pp.351-361, Springer, 2008. https://doi.org/10.1007/978-3-642-01044-6_32, preprint

Haasdonk, B., Pekalska, E.: Indefinite Kernel Fisher Discriminant. In Proc. of ICPR 2008, International Conference on Pattern Recognition, IEEE, 2008. https://doi.org/10.1109/ICPR.2008.4761718, preprint

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

Haasdonk, B., Burkhardt, H.: Classification with Invariant Distance Substitution Kernels. In Proc. of 31st GfKl Conference, Data Analysis, Machine Learning and Applications, 37–44, Springer, 2008. https://doi.org/10.1007/978-3-540-78246-9_5, preprint

2007

Haasdonk, B., Burkhardt, H.: Invariant Kernels for Pattern Analysis and Machine Learning. Machine Learning, 68:35–61, 2007. https://doi.org/10.1007/s10994-007-5009-7, preprint

Haasdonk, B., Ohlberger, M.: Basis Construction for Reduced Basis Methods by Adaptive Parameter Grids. In Proc. ADMOS 2007, International Conference on Adaptive Modeling and Simulation, pp. 116–119, CIMNE, 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, pp. 288-293, ACM 2006. preprint

2005

Haasdonk, B.: Feature Space Interpretation of SVMs with Indefinite Kernels. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(4):482–492, 2005. https://doi.org/10.1109/TPAMI.2005.78, electronic version (C) IEEE notice, appendix, preprint

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. https://doi.org/10.1007/11499145_85, preprint

2004

Haasdonk, B., Keysers, D.: Tangent Distance Kernels for Support Vector Machines. ICPR 2002, International Conference on Pattern Recognition, 2:864-868, IEEE, 2004. https://doi.org/10.1109/ICPR.2002.1048439, electronic version (C) IEEE notice

Haasdonk, B., Halawani, A., Burkhardt, H.: Adjustable Invariant Features by Partial Haar-Integration. ICPR 2004, International Conference on Pattern Recognition, pp. 769-774, IEEE, 2004. https://doi.org/10.1109/ICPR.2004.1334372 preprint

Haasdonk, B., Bahlmann, C.: Learning with Distance Substitution Kernels. Pattern Recognition – Proc. of the 26th DAGM Symposium, pp. 220-227, Springer Berlin, 2004. https://doi.org/10.1007/978-3-540-28649-3_27, preprint

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. https://doi.org/10.1007/s00607-003-1476-2, preprint

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. pp 49-54, IEEE, 2002. https://doi.org/10.1109/IWFHR.2002.1030883, preprint, (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. https://doi.org/10.1007/s211-001-8011-x, preprint

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. https://doi.org/10.1007/978-3-0348-8372-6_1,  preprint

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. preprint 

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 the teaching page

See also my google scholar profile.

Citations: 5644 (Google Scholar), 967 (MathSciNet)

h-index: 35 (Google Scholar)

i10: 83 (Google Scholar)

Publications: 209 (Google Scholar), 83 (DBLP.uni-trier.de), 71 (MathSciNet), 85 (Zentralblatt MATH)

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

Funded Projects

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.

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.

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.