Bernard Haasdonk
Bernard Haasdonk
Professor for Numerical Mathematics
Verified email at - Homepage
Cited by
Cited by
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
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
Feature space interpretation of SVMs with indefinite kernels
B Haasdonk
IEEE Transactions on pattern analysis and machine intelligence 27 (4), 482-492, 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
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
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
Learning with distance substitution kernels
B Haasdonk, C Bahlmann
Joint pattern recognition symposium, 220-227, 2004
Convergence rates of the pod–greedy method
B Haasdonk
ESAIM: Mathematical modelling and numerical Analysis 47 (3), 859-873, 2013
Tangent distance kernels for support vector machines
B Haasdonk, D Keysers
2002 International Conference on Pattern Recognition 2, 864-868, 2002
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
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
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
Invariant kernel functions for pattern analysis and machine learning
B Haasdonk, H Burkhardt
Machine learning 68, 35-61, 2007
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
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, 145-168, 2008
Certified PDE-constrained parameter optimization using reduced basis surrogate models for evolution problems
MA Dihlmann, B Haasdonk
Computational Optimization and Applications 60, 753-787, 2015
Convergence rate of the data-independent -greedy algorithm in kernel-based approximation
G Santin, B Haasdonk
arXiv preprint arXiv:1612.02672, 2016
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
The localized reduced basis multiscale method
F Albrecht, B Haasdonk, S Kaulmann, M Ohlberger
A new local reduced basis discontinuous Galerkin approach for heterogeneous multiscale problems
S Kaulmann, M Ohlberger, B Haasdonk
Comptes Rendus Mathematique 349 (23-24), 1233-1238, 2011
The system can't perform the operation now. Try again later.
Articles 1–20