Publications

Publications of our group

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

  1. 2024

    1. P. Buchfink, S. Glas, B. Haasdonk, and B. Unger, “Model reduction on manifolds: A differential geometric framework.” 2024. [Online]. Available: https://arxiv.org/abs/2312.01963
    2. F. Döppel, T. Wenzel, R. Herkert, B. Haasdonk, and M. Votsmeier, “Goal‐Oriented Two‐Layered Kernel Models as Automated Surrogates for Surface Kinetics in Reactor Simulations,” Chemie Ingenieur Technik, vol. 96, Art. no. 6, Jan. 2024, doi: 10.1002/cite.202300178.
    3. M. Hammer et al., “A new method to design energy-conserving surrogate models for the coupled, nonlinear responses of intervertebral discs,” Biomechanics and Modeling in Mechanobiology, vol. 23, Art. no. 3, Jun. 2024, doi: 10.1007/s10237-023-01804-4.
    4. R. Herkert, P. Buchfink, T. Wenzel, B. Haasdonk, P. Toktaliev, and O. Iliev, “Greedy Kernel Methods for Approximating Breakthrough Curves for Reactive Flow from 3D Porous Geometry Data,” Mathematics, vol. 12, Art. no. 13, 2024, doi: 10.3390/math12132111.
    5. R. R. Herkert, “Replication Code for: Greedy Kernel Methods for Approximating Breakthrough Curves for Reactive Flow from 3D Porous Geometry Data.” 2024. doi: 10.18419/darus-4227.
    6. T. Wenzel, B. Haasdonk, H. Kleikamp, M. Ohlberger, and F. Schindler, “Application of Deep Kernel Models for Certified and Adaptive RB-ML-ROM Surrogate Modeling,” in Large-Scale Scientific Computations, I. Lirkov and S. Margenov, Eds., Cham: Springer Nature Switzerland, 2024, pp. 117–125.

  2. 2023

    1. P. Buchfink, S. Glas, and B. Haasdonk, “Approximation Bounds for Model Reduction on Polynomially Mapped Manifolds.” 2023. [Online]. Available: https://arxiv.org/abs/2312.00724
    2. B. Haasdonk, H. Kleikamp, M. Ohlberger, F. Schindler, and T. Wenzel, “A New Certified Hierarchical and Adaptive RB-ML-ROM Surrogate Model for Parametrized PDEs,” SIAM Journal on Scientific Computing, vol. 45, Art. no. 3, May 2023, doi: 10.1137/22m1493318.
    3. G. Santin, T. Wenzel, and B. Haasdonk, “On the optimality of target-data-dependent kernel greedy interpolation in Sobolev Reproducing Kernel Hilbert Spaces.” 2023. [Online]. Available: https://arxiv.org/abs/2307.09811
    4. T. Wenzel, G. Santin, and B. Haasdonk, “Analysis of Target Data-Dependent Greedy Kernel Algorithms: Convergence Rates for f -, f · P - and f /P -greedy,” Constructive Approximation, vol. 57, Art. no. 1, Feb. 2023, doi: 10.1007/s00365-022-09592-3.
    5. T. Wenzel, G. Santin, and B. Haasdonk, “Stability of convergence rates: Kernel interpolation on non-Lipschitz domains.” 2023. doi: https://doi.org/10.1093/imanum/drae014.

  3. 2022

    1. P. Buchfinck, S. Glas, and B. Haasdonk, “Optimal Bases for Symplectic Model Order Reduction of Canonizable Linear Hamiltonian Systems,” 2022.
    2. P. Gavrilenko et al., “A Full Order, Reduced Order and Machine Learning Model Pipeline for Efficient Prediction of Reactive Flows,” in Large-Scale Scientific Computing, I. Lirkov and S. Margenov, Eds., Cham: Springer International Publishing, 2022, pp. 378–386.
    3. B. Haasdonk, H. Kleikamp, M. Ohlberger, F. Schindler, and T. Wenzel, “A new certified hierarchical and adaptive RB-ML-ROM surrogate model for parametrized PDEs.” arXiv, 2022. doi: 10.48550/ARXIV.2204.13454.
    4. J. Rettberg et al., “Port-Hamiltonian Fluid-Structure Interaction Modeling and Structure-Preserving Model Order Reduction of a Classical Guitar.” 2022. doi: https://doi.org/10.48550/arXiv.2203.10061.
    5. G. Santin, T. Karvonen, and B. Haasdonk, “Sampling based approximation of linear functionals in reproducing kernel Hilbert spaces,” BIT Numerical Mathematics, vol. 62, Art. no. 1, Mar. 2022, doi: 10.1007/s10543-021-00870-3.
    6. S. Shuva, P. Buchfink, O. Röhrle, and B. Haasdonk, “Reduced Basis Methods for Efficient Simulation of a Rigid Robot Hand Interacting with Soft Tissue,” in Large-Scale Scientific Computing, I. Lirkov and S. Margenov, Eds., Springer International Publishing, 2022, pp. 402–409.
    7. T. Wenzel, M. Kurz, A. Beck, G. Santin, and B. Haasdonk, “Structured Deep Kernel Networks for Data-Driven Closure Terms of Turbulent Flows,” in Large-Scale Scientific Computing, I. Lirkov and S. Margenov, Eds., Cham: Springer International Publishing, 2022, pp. 410–418.
    8. T. Wenzel, G. Santin, and B. Haasdonk, “Stability of convergence rates: Kernel interpolation on non-Lipschitz domains.” arXiv, 2022. doi: 10.48550/ARXIV.2203.12532.
    9. T. Wenzel, G. Santin, and B. Haasdonk, “Analysis of Target Data-Dependent Greedy Kernel Algorithms: Convergence Rates for f-, \$\$f \backslashcdot P\$\$- and f/P-Greedy,” Constructive Approximation, Oct. 2022, doi: 10.1007/s00365-022-09592-3.

  4. 2021

    1. D. Wittwar and B. Haasdonk, “Convergence rates for matrix P-greedy variants,” in Numerical mathematics and advanced applications---ENUMATH 2019, vol. 139, in Lect. Notes Comput. Sci. Eng., vol. 139. , Springer, Cham, pp. 1195–1203. doi: 10.1007/978-3-030-55874-1\_119.
    2. P. Buchfink, S. Glas, and B. Haasdonk, “Symplectic Model Reduction of Hamiltonian Systems on Nonlinear Manifolds.” 2021. doi: https://doi.org/10.48550/arXiv.2112.10815.
    3. P. Buchfink and B. Haasdonk, “Experimental Comparison of Symplectic and Non-symplectic Model Order Reduction an Uncertainty Quantification Problem,” in Numerical Mathematics and Advanced Applications ENUMATH 2019, F. J. Vermolen and C. Vuik, Eds., Springer International Publishing, 2021. doi: 10.1007/978-3-030-55874-1.
    4. T. Ehring and B. Haasdonk, “Greedy sampling and approximation for realizing feedback control for high dimensional nonlinear systems,” 2021.
    5. T. Ehring and B. Haasdonk, “Feedback control for a coupled soft tissue system by kernel surrogates,” in Coupled Problems 2021, 2021. doi: 10.23967/coupled.2021.026.
    6. B. Haasdonk, B. Hamzi, G. Santin, and D. Wittwar, “Kernel methods for center manifold approximation and a weak data-based version of the center manifold theorem,” Phys. D, vol. 427, p. Paper No. 133007, 14, 2021, doi: 10.1016/j.physd.2021.133007.
    7. B. Haasdonk, “Model Order Reduction, Applications, MOR Software,” vol. 3, D. Gruyter, Ed., De Gruyter, 2021. doi: 10.1515/9783110499001.
    8. B. Haasdonk, M. Ohlberger, and F. Schindler, “An adaptive model hierarchy for data-augmented training of kernel models for reactive flow.” arXiv, 2021. doi: 10.48550/ARXIV.2110.12388.
    9. B. Haasdonk, T. Wenzel, G. Santin, and S. Schmitt, “Biomechanical Surrogate Modelling Using Stabilized Vectorial Greedy Kernel Methods,” 2021.
    10. R. Leiteritz, P. Buchfink, B. Haasdonk, and D. Pflüger, “Surrogate-data-enriched Physics-Aware Neural Networks.” 2021.
    11. G. Santin and B. Haasdonk, “Kernel methods for surrogate modeling,” in Model Order Reduction, vol. 1: System- and Data-Driven Methods and Algorithms, P. Benner, W. Schilders, S. Grivet-Talocia, A. Quarteroni, G. Rozza, and L. M. Silveira, Eds., de Gruyter, 2021, pp. 311–354.
    12. T. Wenzel, G. Santin, and B. Haasdonk, “A novel class of stabilized greedy kernel approximation algorithms: Convergence, stability and uniform point distribution,” 2021.
    13. T. Wenzel, G. Santin, and B. Haasdonk, “Universality and Optimality of Structured Deep Kernel Networks.” arXiv, 2021. doi: 10.48550/ARXIV.2105.07228.
    14. T. Wenzel, G. Santin, and B. Haasdonk, “Analysis of target data-dependent greedy kernel algorithms: Convergence rates for $f$-, $f P$- and $f/P$-greedy.” arXiv, 2021. doi: 10.48550/ARXIV.2105.07411.
    15. T. Wenzel, G. Santin, and B. Haasdonk, “Analysis of target data-dependent greedy kernel algorithms: Convergence rates for f-, f P- and f/P-greedy.” arXiv, 2021. doi: 10.48550/ARXIV.2105.07411.

  5. 2020

    1. A. Alla, B. Haasdonk, and A. Schmidt, “Feedback control of parametrized PDEs via model order reduction and dynamic programming principle,” Adv. Comput. Math., vol. 46, Art. no. 1, 2020, doi: 10.1007/s10444-020-09744-8.
    2. P. Buchfink, B. Haasdonk, and S. Rave, “PSD-Greedy Basis Generation for Structure-Preserving Model Order Reduction of Hamiltonian Systems,” in Proceedings of the Conference Algoritmy 2020, P. Frolkovič, K. Mikula, and D. Ševčovič, Eds., Vydavateľstvo SPEKTRUM, Aug. 2020, pp. 151–160. [Online]. Available: http://www.iam.fmph.uniba.sk/amuc/ojs/index.php/algoritmy/article/view/1577/829
    3. J. Fehr and B. Haasdonk, Eds., IUTAM Symposium on Model Order Reduction of Coupled Systems, Stuttgart, Germany, May 22-25, 2018: MORCOS 2018. in IUTAM Bookseries. Springer, 2020.
    4. D. Grunert, J. Fehr, and B. Haasdonk, “Well-scaled, a-posteriori error estimation for model order reduction of large second-order mechanical systems,” ZAMM, vol. 100, Art. no. 8, 2020, doi: 10.1002/zamm.201900186.
    5. B. Haasdonk, B. Hamzi, G. Santin, and D. Wittwar, “Greedy kernel methods for center manifold approximation,” in Spectral and high order methods for partial differential equations---ICOSAHOM 2018, vol. 134, in Lect. Notes Comput. Sci. Eng., vol. 134. , Springer, Cham, 2020, pp. 95–106. doi: 10.1007/978-3-030-39647-3\_6.

  6. 2019

    1. A. Bhatt, J. Fehr, D. Grunert, and B. Haasdonk, “A Posteriori Error Estimation in Model Order Reduction of Elastic Multibody Systems with Large Rigid Motion,” in IUTAM Symposium on Model Order Reduction of Coupled Systems, Stuttgart, Germany, May 22–25, 2018, J. Fehr and B. Haasdonk, Eds., Springer, 2019. doi: DOI:10.1007/978-3-030-21013-7_7.
    2. A. Bhatt, J. Fehr, and B. Haasdonk, “Model order reduction of an elastic body under large rigid motion,” Proceedings of ENUMATH 2017, vol. Lect. Notes Comput. Sci. Eng., Art. no. 126, 2019, doi: 10.1007/978-3-319-96415-7\_23.
    3. T. Brünnette, G. Santin, and B. Haasdonk, “Greedy Kernel Methods for Accelerating Implicit Integrators for Parametric ODEs,” in Numerical Mathematics and Advanced Applications - ENUMATH 2017, F. A. Radu, K. Kumar, I. Berre, J. M. Nordbotten, and I. S. Pop, Eds., Cham: Springer International Publishing, 2019, pp. 889–896.
    4. P. Buchfink, A. Bhatt, and B. Haasdonk, “Symplectic Model Order Reduction with Non-Orthonormal Bases,” Mathematical and Computational Applications, vol. 24, Art. no. 2, 2019, doi: 10.3390/mca24020043.
    5. K. Carlberg, L. Brencher, B. Haasdonk, and A. Barth, “Data-Driven Time Parallelism via Forecasting,” SIAM Journal on Scientific Computing, vol. 41, Art. no. 3, 2019, doi: 10.1137/18M1174362.
    6. A. Denzel, B. Haasdonk, and J. Kästner, “Gaussian Process Regression for Minimum Energy Path Optimization and Transition State Search,” J. Phys. Chem. A, vol. 123, Art. no. 44, 2019, [Online]. Available: https://doi.org/10.1021/acs.jpca.9b08239
    7. R. Föll, B. Haasdonk, M. Hanselmann, and H. Ulmer, “Deep Recurrent Gaussian Process with Variational Sparse Spectrum Approximation.” 2019. [Online]. Available: https://openreview.net/forum?id=BkgosiRcKm
    8. M. Köppel et al., “Comparison of data-driven uncertainty quantification methods for a carbon dioxide storage benchmark scenario,” Comput. Geosci., vol. 2, Art. no. 23, 2019, doi: https://doi.org/10.1007/s10596-018-9785-x.
    9. G. Santin and B. Haasdonk, “Kernel Methods for Surrogate Modelling,” University of Stuttgart, 2019.
    10. G. Santin and B. Haasdonk, “Kernel Methods for Surrogate Modeling,” ArXiv 1907.10556, 2019. [Online]. Available: https://arxiv.org/abs/1907.10556
    11. G. Santin, D. Wittwar, and B. Haasdonk, “Sparse approximation of regularized kernel interpolation by greedy algorithms,” 2019.
    12. A. Schmidt, D. Wittwar, and B. Haasdonk, “Rigorous and effective a-posteriori error bounds for nonlinear problems -- Application to RB methods,” Advances in Computational Mathematics, 2019, doi: 10.1007/s10444-019-09730-9.
    13. T. Wenzel, G. Santin, and B. Haasdonk, “A novel class of stabilized greedy kernel approximation algorithms: Convergence, stability & uniform point distribution.” 2019.
    14. D. Wittwar and B. Haasdonk, “Greedy Algorithms for Matrix-Valued Kernels,” in Numerical Mathematics and Advanced Applications ENUMATH 2017, F. A. Radu, K. Kumar, I. Berre, J. M. Nordbotten, and I. S. Pop, Eds., Cham: Springer International Publishing, 2019, pp. 113–121.
    15. D. Wittwar, G. Santin, and B. Haasdonk, “Part II on matrix valued kernels including analysis,” 2019.

  7. 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, J. Fehr, D. Grunert, and B. Haasdonk, “A Posteriori Error Estimation in Model Order Reduction of Elastic Multibody Systems with Large Rigid Motion,” in IUTAM Symposium on Model Order Reduction of Coupled Systems, Stuttgart, Germany, May 22–25, 2018, J. Fehr and B. Haasdonk, Eds., Springer, 2018. doi: DOI:10.1007/978-3-030-21013-7_7.
    3. A. Bhatt and B. Haasdonk, “Certified and structure-preserving model order reduction of EMBS,” RAMSA 2017, New Delhi. 2018.
    4. A. Bhatt, B. Haasdonk, and B. E. Moore, “Structure-preserving Integration and Model Order Reduction,” Invited online talk in Department of Mathematics, IIT Roorkee. 2018.
    5. T. Brünnette, G. Santin, and B. Haasdonk, “Greedy kernel methods for accelerating implicit integrators for parametric ODEs,” in Proc. ENUMATH 2017, 2018.
    6. C. Dibak, B. Haasdonk, A. Schmidt, F. Dürr, and K. Rothermel, “Enabling interactive mobile simulations through distributed reduced models,” Pervasive and Mobile Computing, Elsevier BV, vol. 45, pp. 19–34, 2018, doi: https://doi.org/10.1016/j.pmcj.2018.02.002.
    7. J. Fehr, D. Grunert, A. Bhatt, and B. Haasdonk, “A Sensitivity Study of Error Estimation in Reduced Elastic Multibody Systems,” in Proceedings of MATHMOD 2018, Vienna, Austria, 2018.
    8. 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, Art. no. 1, 2018, doi: doi:10.3390/mca23010008.
    9. B. Haasdonk, B. Hamzi, G. Santin, and D. Wittwar, “Greedy Kernel Methods for Center Manifold Approximation,” ArXiv 1810.11329, 2018.
    10. 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. doi: 10.1007/978-3-319-75319-5_2.
    11. 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, Art. no. ja, 2018, doi: 10.1002/cnm.3095.
    12. G. Santin, D. Wittwar, and B. Haasdonk, “Greedy regularized kernel interpolation,” University of Stuttgart, ArXiv preprint 1807.09575, 2018.
    13. A. Schmidt and B. Haasdonk, “Data-driven surrogates of value functions and applications to feedback control for dynamical systems,” 2018. [Online]. Available: http://www.simtech.uni-stuttgart.de/publikationen/prints.php?ID=1766
    14. A. Schmidt, D. Wittwar, and B. Haasdonk, “Rigorous and effective a-posteriori error bounds for nonlinear problems -- Application to RB methods,” University of Stuttgart, SimTech Preprint, 2018.
    15. A. Schmidt and B. Haasdonk, “Reduced basis approximation of large scale parametric algebraic Riccati equations,” ESAIM: Control, Optimisation and Calculus of Variations, vol. 24, Art. no. 1, Jan. 2018, doi: 10.1051/cocv/2017011.
    16. D. Wittwar, G. Santin, and B. Haasdonk, “Interpolation with uncoupled separable matrix-valued kernels,” ArXiv e-prints, Jul. 2018.
    17. 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

  8. 2017

    1. A. Alla, M. Gunzburger, B. Haasdonk, and A. Schmidt, “Model order reduction for the control of parametrized partial differential equations via dynamic programming principle,” University of Stuttgart, 2017.
    2. A. Alla, B. Haasdonk, and A. Schmidt, “Feedback control of parametrized PDEs via model order reduction and dynamic programming principle,” University of Stuttgart, 2017. [Online]. Available: http://www.simtech.uni-stuttgart.de/publikationen/prints.php?ID=1765
    3. 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. doi: 10.1007/978-3-319-58786-8_21.
    4. 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: Theory and Algorithms, P. Benner, A. Cohen, M. Ohlberger, and K. Willcox, Eds., SIAM Philadelphia, 2017. [Online]. Available: https://www2.mpi-magdeburg.mpg.de/preprints/2015/MPIMD15-01.pdf
    5. T. Brünnette, G. Santin, and B. Haasdonk, “Greedy kernel methods for accelerating implicit integrators for parametric ODEs,” University of Stuttgart, 2017. [Online]. Available: http://www.simtech.uni-stuttgart.de/publikationen/prints.php?ID=1767
    6. C. Dibak, A. Schmidt, F. Dürr, B. Haasdonk, and K. Rothermel, “Server-assisted interactive mobile simulations for pervasive applications,” in 2017 IEEE International Conference on Pervasive Computing and Communications (PerCom), Mar. 2017, pp. 111–120. doi: 10.1109/PERCOM.2017.7917857.
    7. J. Fehr, D. Grunert, A. Bhatt, and B. Hassdonk, “A Sensitivity Study of Error Estimation in Reduced Elastic Multibody Systems,” in Proceedings of MATHMOD 2018, Vienna, Austria, 2017.
    8. 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. [Online]. Available: http://www.simtech.uni-stuttgart.de/publikationen/prints.php?ID=938
    9. M. Köppel et al., “Comparison of data-driven uncertainty quantification methods for a carbon dioxide storage benchmark scenario,” University of Stuttgart, 2017. [Online]. Available: http://www.simtech.uni-stuttgart.de/publikationen/prints.php?ID=1759
    10. M. Köppel et al., “Datasets and executables of data-driven uncertainty quantification benchmark in carbon dioxide storage.” Nov. 2017. doi: 10.5281/zenodo.933827.
    11. T. Köppl, G. Santin, B. Haasdonk, and R. Helmig, “Numerical modelling of a peripheral arterial stenosis using dimensionally reduced models and kernel methods,” University of Stuttgart, 2017.
    12. 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.
    13. 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
    14. G. Santin and B. Haasdonk, “Greedy Kernel Approximation for Sparse Surrogate Modelling,” University of Stuttgart, 2017.
    15. G. Santin and B. Haasdonk, “Non-symmetric kernel greedy interpolation.,” 2017.
    16. 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
    17. A. Schmidt and B. Haasdonk, “Reduced basis approximation of large scale parametric algebraic Riccati equations,” ESAIM: Control, Optimisation and Calculus of Variations, Feb. 2017, doi: 10.1051/cocv/2017011.
    18. P. Tempel, A. Schmidt, B. Haasdonk, and A. Pott, “Application of the Rigid Finite Element Method to the Simulation of Cable-Driven Parallel Robots,” University of Stuttgart, 2017.
    19. D. Wittwar and B. Haasdonk, “On uncoupled separable matrix-valued kernels,” University of Stuttgart, 2017.
    20. D. Wittwar, G. Santin, and B. Haasdonk, “Interpolation with uncoupled separable matrix-valued kernels.,” ArXiv preprint 1807.09111, Accepted for publications in Dolomites Res. Notes Approx., 2017.
    21. D. Wittwar, A. Schmidt, and B. Haasdonk, “Reduced Basis Approximation for the Discrete-time Parametric Algebraic Riccati Equation,” University of Stuttgart, 2017.

  9. 2016

    1. A. Alla, A. Schmidt, and B. Haasdonk, “Model order reduction approaches for infinite horizon optimal control problems via the HJB equation,” University of Stuttgart, Jul. 2016. [Online]. Available: https://arxiv.org/abs/1607.02337
    2. D. Amsallem and B. Haasdonk, “PEBL-ROM: Projection-Error Based Local Reduced-Order Models,” AMSES, Advanced Modeling and Simulation in Engineering Sciences, vol. 3, Art. no. 6, 2016, doi: 10.1186/s40323-016-0059-7.
    3. A. C. Antoulas, B. Haasdonk, and B. Peherstorfer, MORML 2016 Book of Abstracts. University of Stuttgart, 2016.
    4. 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. [Online]. Available: https://www2.mpi-magdeburg.mpg.de/preprints/2015/MPIMD15-01.pdf
    5. M. Dihlmann and B. Haasdonk, “A reduced basis Kalman filter for parametrized partial differential equations,” ESAIM: COCV, vol. 22, Art. no. 3, 2016, doi: 10.1051/cocv/2015019.
    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, Arxiv Report, 2016. [Online]. Available: https://arxiv.org/abs/1610.05029
    7. D. Garmatter, B. Haasdonk, and B. Harrach, “A reduced Landweber Method for Nonlinear Inverse Problems,” Inverse Problems, vol. 32, Art. no. 3, 2016, doi: http://dx.doi.org/10.1088/0266-5611/32/3/035001.
    8. 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, doi: 10.1080/13873954.2016.1198384.
    9. A. Schmidt and B. Haasdonk, “Reduced basis method for H2 optimal feedback control problems,” IFAC-PapersOnLine, vol. 49, Art. no. 8, 2016, doi: http://dx.doi.org/10.1016/j.ifacol.2016.07.462.

  10. 2015

    1. D. Amsallem, C. Farhat, and B. Haasdonk, “Special Issue on Model Reduction,” IJNME, International Journal of Numerical Methods in Engineering, vol. 102, Art. no. 5, 2015, doi: 10.1002/nme.4889.
    2. D. Amsallem, C. Farhat, and B. Haasdonk, “Editorial: Special Issue on Model Reduction,” IJNME, International Journal of Numerical Methods in Engineering, vol. 102, Art. no. 5, 2015, doi: 10.1002/nme.4889.
    3. D. Amsallem and B. Haasdonk, “PEBL-ROM: Projection-Error Based Local Reduced-Order Models,” University of Stuttgart, SimTech Preprint, Oct. 2015. [Online]. Available: http://www.simtech.uni-stuttgart.de/publikationen/prints.php?ID=1436
    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), vol. 6, Art. no. 1, 2015, doi: 10.1137/140981216.
    5. 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.
    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, Art. no. 3, 2015, doi: DOI: 10.1007/s10589-014-9697-1.
    7. D. Garmatter, B. Haasdonk, and B. Harrach, “A reduced Landweber Method for Nonlinear Inverse Problems,” University of Stuttgart, 2015.
    8. 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, Art. no. 5, SI, May 2015, doi: 10.1002/nme.4773.
    9. I. Martini and B. Haasdonk, “Output Error Bounds for the Dirichlet-Neumann Reduced Basis Method,” in Numerical Mathematics and Advanced Applications - ENUMATH 2013, in Lecture Notes in Computational Science and Engineering, vol. 103. 2015, pp. 437–445. doi: 10.1007/978-3-319-10705-9_43.
    10. 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, Art. no. 5, 2015, doi: 10.1007/s10444-014-9396-6.
    11. M. Redeker and B. Haasdonk, “A POD-EIM reduced two-scale model for crystal growth,” Advances in Computational Mathematics, vol. 41, Art. no. 5, 2015, doi: 10.1007/s10444-014-9367-y.
    12. M. Redeker and B. Haasdonk, “A POD-EIM reduced two-scale model for precipitation in porous media,” University of Stuttgart, SimTech Preprint, 2015. [Online]. Available: http://www.simtech.uni-stuttgart.de/publikationen/prints.php?ID=964
    13. 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.
    14. A. Schmidt and B. Haasdonk, “Reduced basis method for $H_2$ optimal feedback control problems,” University of Stuttgart, 2015. [Online]. Available: http://www.simtech.uni-stuttgart.de/publikationen/prints.php?ID=1442
    15. A. Schmidt and B. Haasdonk, “Reduced Basis Approximation of Large Scale Algebraic Riccati Equations,” University of Stuttgart, 2015.
    16. D. Wirtz, N. Karajan, and B. Haasdonk, “Surrogate Modelling of multiscale models using kernel methods,” International Journal of Numerical Methods in Engineering, vol. 101, Art. no. 1, 2015, doi: 10.1002/nme.4767.
    17. D. Wirtz, N. Karajan, and B. Haasdonk, “Surrogate modeling of multiscale models using kernel methods,” INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING, vol. 101, Art. no. 1, Jan. 2015, doi: 10.1002/nme.4767.

  11. 2014

    1. 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.
    2. M. Dihlmann and B. Haasdonk, “A reduced basis Kalman filter for parametrized partial differential equations,” University of Stuttgart, 2014.
    3. B. Haasdonk, “Reduced Basis Methods for Parametrized PDEs -- A Tutorial Introduction for Stationary and Instationary Problems,” IANS, University of Stuttgart, Germany, SimTech Preprint, 2014. [Online]. Available: http://www.simtech.uni-stuttgart.de/publikationen/prints.php?ID=938
    4. B. Haasdonk and M. Ohlberger, “Wenn die Probleme zahlreicher werden: Reduzierte Basis Methoden f�r effiziente und gesicherte numerische Simulation,” GAMM Rundbrief, vol. 2014, Art. no. 1, 2014.
    5. B. Haasdonk and M. Ohlberger, “Wenn die Probleme zahlreicher werden: Reduzierte Basis Methoden für effiziente und gesicherte numerische Simulation,” GAMM Rundbrief, vol. 2014, Art. no. 1, 2014.
    6. 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, Sep. 2014, doi: 10.1002/nme.4773.
    7. S. Kaulmann, B. Flemisch, B. Haasdonk, K. A. Lie, and M. Ohlberger, “The Localized Reduced Basis Multiscale method for two-phase flow in porous media,” arXiv preprint arXiv:1405.2810, 2014.
    8. 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.
    9. 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, Art. no. 2, 2014, doi: 10.1137/120899042.

  12. 2013

    1. D. Amsallem, B. Haasdonk, and G. Rozza, “A Conference within a Conference for MOR Researchers,” SIAM News, vol. 46, Art. no. 6, Jul. 2013, [Online]. Available: http://www.siam.org/news/news.php?id=2089
    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. Cvetkovic, T. Atanackovic and V. Kostic, vol. 13, Art. no. 1, 2013, doi: doi: 10.1002/pamm.201310002.
    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), SimTech Preprint, 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, Art. no. 8, 2013, doi: 10.1002/zamm.201200014.
    5. B. Haasdonk, “Convergence Rates of the POD--Greedy Method,” ESAIM: Mathematical Modelling and Numerical Analysis, vol. 47, Art. no. 3, 2013, doi: 10.1051/m2an/2012045.
    6. 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.
    7. S. Kaulmann and B. Haasdonk, “Online Greedy Reduced Basis Construction using Dictionaries,” University of Stuttgart, SimTech Preprint, 2013.
    8. 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
    9. 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

  13. 2012

    1. F. Albrecht, B. Haasdonk, S. Kaulmann, and M. Ohlberger, “The Localized Reduced Basis Multiscale Method,” in ALGORITMY 2012 - Proceedings of contributed papers and posters, A. Handlovicova, Z. Minarechova, and D. Cevcovic, Eds., Publishing House of STU, Apr. 2012, pp. 393–403. [Online]. Available: http://www.iam.fmph.uniba.sk/algoritmy2012/zbornik/40Albrecht.pdf
    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 Approximation for Nonlinear Parametrized Evolution Equations based on Empirical Operator Interpolation,” SIAM J. Sci. Comput., vol. 34, Art. no. 2, 2012, doi: 10.1137/10081157X.
    4. 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. doi: https://doi.org/10.3182/20120215-3-AT-3016.00128.
    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. [Online]. Available: http://www.springer.com/engineering/computational+intelligence+and+complexity/book/978-3-642-28588-2
    6. B. Haasdonk, J. Salomon, and B. Wohlmuth, “A Reduced Basis Method for Parametrized Variational Inequalities,” SIAM Journal on Numerical Analysis, vol. 50, Art. no. 5, 2012.
    7. B. Haasdonk, J. Salomon, and B. Wohlmuth, “A Reduced Basis Method for the Simulation of American Options,” in ENUMATH 2011 Proceedings, 2012. [Online]. Available: http://arxiv.org/pdf/1201.3289v1
    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, [Online]. Available: http://www.biomedcentral.com/1752-0509/6/81
    10. 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
    11. D. Wirtz and B. Haasdonk, “Efficient a-posteriori error estimation for nonlinear kernel-based reduced systems,” Systems and Control Letters, vol. 61, Art. no. 1, 2012, doi: 10.1016/j.sysconle.2011.10.012.
    12. D. Wirtz, N. Karajan, and B. Haasdonk, “Model order reduction of multiscale models using kernel methods,” SRC SimTech, University of Stuttgart, Germany, Preprint, Jun. 2012.
    13. D. Wirtz, D. C. Sorensen, and B. Haasdonk, “A-posteriori error estimation for DEIM reduced nonlinear dynamical systems,” University of Stuttgart, SimTech Preprint, Oct. 2012. [Online]. Available: http://www.simtech.uni-stuttgart.de/publikationen/prints.php?ID=733
    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. [Online]. Available: http://www.ifac-papersonline.net/

  14. 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, “Reduzierte-Basis-Methoden, Vorlesungsskript SS 2011,” University of Stuttgart, IANS-Report 2011–004, 2011.
    4. 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.
    5. B. Haasdonk and B. Lohmann, “Special Issue on ″Model Order Reduction of Parametrized Problems″,” Mathematical and Computer Modelling of Dynamical Systems, vol. 17, Art. no. 4, 2011, doi: 10.1080/13873954.2011.547661.
    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, Art. no. 2, 2011, doi: 10.1080/13873954.2010.514703.
    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, Art. no. 4, 2011, doi: 10.1080/13873954.2011.582120.
    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, Art. no. 23–24, Dec. 2011, doi: 10.1016/j.crma.2011.10.024.

  15. 2010

    1. M. Drohmann, B. Haasdonk, and M. Ohlberger, “Reduced Basis Approximation for Nonlinear Parametrized Evolution Equations based on Empirical Operator Interpolation,” University of Münster, Preprint Angewandte Mathematik und Informatik 02/10-N, 2010.
    2. B. Haasdonk, “Effiziente und Gesicherte Modellreduktion für Parametrisierte Dynamische Systeme.,” at - Automatisierungstechnik, vol. 58, Art. no. 8, 2010.
    3. B. Haasdonk, M. Dihlmann, and M. Ohlberger, “A Training Set and Multiple Bases Generation Approach for Parametrized Model Reduction Based on Adaptive Grids in Parameter Space.,” University of Stuttgart, 2010.
    4. E. Pekalska and B. Haasdonk, “Indefinite Kernel Discriminant Analysis,” in Proc. COMPSTAT 2010, International Conference on Computational Statistics, 2010.

  16. 2009

    1. M. Drohmann, B. Haasdonk, and M. Ohlberger, “Reduced Basis Method for Finite Volume Approximation of Evolution Equations on Parametrized Geometries,” 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. [Online]. Available: http://www.ians.uni-stuttgart.de/am/Haasdonk/publications/mathmod2009_Nadapt.pdf
    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. [Online]. Available: http://www.ians.uni-stuttgart.de/am/Haasdonk/publications/mathmod2009_PMOR.pdf
    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, in Proc. Sympos. Appl. Math., vol. 67. , Providence, RI: Amer. Math. Soc., 2009, pp. 605–614.
    6. B. Haasdonk, M. Ohlberger, T. Tonn, and K. Urban, MoRePaS 2009 Book of Abstracts. University of Münster, 2009.
    7. N. Jung, B. Haasdonk, and D. Kröner, “Reduced Basis Method for Quadratically Nonlinear Transport Equations,” IJCSM, vol. 2, Art. no. 4, 2009.
    8. E. Pekalska and B. Haasdonk, “Kernel Discriminant Analysis with Positive Definite and Indefinite Kernels,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, Art. no. 6, 2009.

  17. 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. [Online]. Available: http://pc2.iam.fmph.uniba.sk/amuc/_contributed/algo2009/drohmann.pdf
    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 M. Ohlberger, “Reduced basis method for finite volume approximations of parametrized linear evolution equations,” ESAIM: M2AN, vol. 42, Art. no. 2, Mar. 2008, doi: 10.1051/m2an:2008001.
    4. 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
    5. B. Haasdonk and E. Pekalska, “Indefinite Kernel Fisher Discriminant,” in Proc. ICPR 2008, International Conference on Pattern Recognition, 2008.
    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. 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.

  18. 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 and H. Burkhardt, “Classification with Invariant Distance Substitution Kernels,” in Proc. of 31st GfKl Conference, Data Analysis, Machine Learning, and Applications, 2007.
    3. 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.
    4. 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, P. Díez and K. Runesson, Eds., CIMNE, Barcelona, 2007.
    5. 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.

  19. 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. B. Haasdonk, R. Klöfkorn, M. Ohlberger, J. Schumacher, and K. Steinkamp, “Complete 3D-Modelling of a PEM Fuel Cell and Stack,” Universität Freiburg, Abteilung für Angewandte Mathematik, 2006.
    3. 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.

  20. 2005

    1. 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.
    2. B. Haasdonk, “Feature Space Interpretation of SVMs with Indefinite Kernels,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, Art. no. 4, 2005, doi: http://doi.ieeecomputersociety.org/10.1109/TPAMI.2005.78.
    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, Springer, 2005.
    4. B. Haasdonk and H. Burkhardt, “Invariant Kernels for Pattern Analysis and Machine Learning,” IIF-LMB, Universität Freiburg, Institut für Informatik, 3/05, Aug. 2005.

  21. 2004

    1. B. Haasdonk and C. Bahlmann, “Learning with Distance Substitution Kernels,” in Pattern Recognition - Proceedings of the 26th DAGM Symposium, Springer, 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, pp. 769–774. doi: http://dx.doi.org/10.1109/ICPR.2004.1334372.

  22. 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, Universit�t Freiburg, 2/03, Nov. 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, pp. 181–204, 2003.

  23. 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, IEEE Computer Society, 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, IEEE Computer Society, 2002, pp. 864–868.

  24. 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, Art. no. 3, 2001, doi: 10.1007/s211-001-8011-x.
    2. 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, Art. no. 3, 2001, doi: 10.1007/s211-001-8011-x.
    3. 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.

  25. 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, Birkh�user, 2000, pp. 475–484.

  26. 1999

    1. T. Ge\sner et al., “A Procedural Interface for Multiresolutional Visualization of General Numerical Data,” University of Bonn, SFB 256 Report 28, 1999.
    2. T. Geßner et al., “A Procedural Interface for Multiresolutional Visualization of General Numerical Data,” University of Bonn, SFB 256 Report 28, 1999.
    3. B. Haasdonk, “Konvergenz eines Staggered Lax-Friedrichs Verfahrens auf unstrukturierten 2D Gittern,” 1999.

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Bernard Haasdonk

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