Bernard Haasdonk

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Mario OhlbergerProfessor for Applied Mathematics, University of Münster, GermanyVerified email at Uni-Muenster.de
Martin DrohmannVerified email at sandia.gov
Gabriele SantinResearcher, Fondazione Bruno KesslerVerified email at fbk.eu
Markus DihlmannUniversity of StuttgartVerified email at mathematik.uni-stuttgart.de
Hans BurkhardtProfessor of Computer Science, University of FreiburgVerified email at informatik.uni-freiburg.de
Elżbieta PękalskaPhD, University of ManchesterVerified email at cs.man.ac.uk
Jörg FehrProfessor at Institute of Engineering and Computational Mechanics and SimTech, University ofVerified email at itm.uni-stuttgart.de
Claus BahlmannUniversity of Freiburg, Siemens Corporate Research, Siemens AGVerified email at siemens.com
Gianluigi RozzaFull Professor of Numerical Analysis, SISSA, Int. School for Adv. Studies, Italy and ERC PIVerified email at sissa.it
Barbara WohlmuthProefssorin für Mathematik, TU MünchenVerified email at ma.tum.de
julien salomonINRIA & Sorbonne UniversitéVerified email at inria.fr
Peter EberhardProfessor, University of Stuttgart, GermanyVerified email at itm.uni-stuttgart.de
Daniel KeysersGoogleVerified email at google.com
Nils KarajanPostDoc for Continuum Biomechanics, SimTech Cluster of Excellence, University of StuttgartVerified email at karajan.de
Martin Rumpfprofessor for mathematics, University of BonnVerified email at uni-bonn.de
Karsten UrbanUlm University, Institute for Numerical MathematicsVerified email at uni-ulm.de
Danny SorensenRice UniversityVerified email at rice.edu
Christian RohdeUniversität StuttgartVerified email at mathematik.uni-stuttgart.de
Peter BennerMax Planck Institute for Dynamics of Complex Technical SystemsVerified email at mpi-magdeburg.mpg.de
Christian HimpeMax Planck Institute for Dynamics of Complex Technical Systems MagdeburgVerified email at wwu.de

Bernard Haasdonk

Professor for Numerical Mathematics

Verified email at mathematik.uni-stuttgart.de - Homepage

Model Order Reduction Reduced Basis Methods Kernel Methods Numerical Analysis Machine Learning


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Online handwriting recognition with support vector machines-a kernel approach C Bahlmann, B Haasdonk, H Burkhardt Proceedings Eighth International Workshop on Frontiers in Handwriting …, 2002	519	2002
Reduced basis method for finite volume approximations of parametrized linear evolution equations B Haasdonk, M Ohlberger ESAIM: Mathematical Modelling and Numerical Analysis 42 (2), 277-302, 2008	431	2008
Feature space interpretation of SVMs with indefinite kernels B Haasdonk IEEE Transactions on pattern analysis and machine intelligence 27 (4), 482-492, 2005	300	2005
Reduced basis approximation for nonlinear parametrized evolution equations based on empirical operator interpolation M Drohmann, B Haasdonk, M Ohlberger SIAM Journal on Scientific Computing 34 (2), A937-A969, 2012	220	2012
Learning with distance substitution kernels B Haasdonk, C Bahlmann Joint pattern recognition symposium, 220-227, 2004	163	2004
A training set and multiple bases generation approach for parameterized model reduction based on adaptive grids in parameter space B Haasdonk, M Dihlmann, M Ohlberger Mathematical and Computer Modelling of Dynamical Systems 17 (4), 423-442, 2011	158	2011
Tangent distance kernels for support vector machines B Haasdonk, D Keysers Object recognition supported by user interaction for service robots 2, 864-868, 2002	152	2002
Convergence rates of the pod–greedy method B Haasdonk ESAIM: Mathematical Modelling and Numerical Analysis 47 (3), 859-873, 2013	133	2013
Kernel discriminant analysis for positive definite and indefinite kernels E Pȩkalska, B Haasdonk IEEE transactions on pattern analysis and machine intelligence 31 (6), 1017-1032, 2008	126	2008
Reduced basis methods for parametrized PDEs–a tutorial introduction for stationary and instationary problems B Haasdonk Model reduction and approximation: theory and algorithms 15, 65, 2017	114	2017
Efficient reduced models and a posteriori error estimation for parametrized dynamical systems by offline/online decomposition B Haasdonk, M Ohlberger Mathematical and Computer Modelling of Dynamical Systems 17 (2), 145-161, 2011	110	2011
A posteriori error estimation for DEIM reduced nonlinear dynamical systems D Wirtz, DC Sorensen, B Haasdonk SIAM Journal on Scientific Computing 36 (2), A311-A338, 2014	108	2014
Invariant kernel functions for pattern analysis and machine learning B Haasdonk, H Burkhardt Machine learning 68 (1), 35-61, 2007	92	2007
A reduced basis method for evolution schemes with parameter-dependent explicit operators B Haasdonk, M Ohlberger, G Rozza ETNA, Electronic Transactions on Numerical Analysis 32 (ARTICLE), 145-168, 2008	78	2008
Model reduction of parametrized evolution problems using the reduced basis method with adaptive time-partitioning M Dihlmann, M Drohmann, B Haasdonk Proc. of ADMOS 2011, 64, 2011	72	2011
Certified PDE-constrained parameter optimization using reduced basis surrogate models for evolution problems MA Dihlmann, B Haasdonk Computational Optimization and Applications 60 (3), 753-787, 2015	61	2015
The localized reduced basis multiscale method F Albrecht, B Haasdonk, S Kaulmann, M Ohlberger	55*
Surrogate modeling of multiscale models using kernel methods D Wirtz, N Karajan, B Haasdonk, E Benvenuti, G Ventura, N Ponara, ... International Journal for Numerical Methods in Engineering 101 (1), 54-78, 0	50*
Adaptive basis enrichment for the reduced basis method applied to finite volume schemes B Haasdonk, M Ohlberger Proc. 5th International Symposium on Finite Volumes for Complex Applications …, 2008	49	2008
Reduced basis methods for parameterized partial differential equations with stochastic influences using the Karhunen--Loève expansion B Haasdonk, K Urban, B Wieland SIAM/ASA Journal on Uncertainty Quantification 1 (1), 79-105, 2013	48	2013

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