Bernard Haasdonk

Dikutip oleh

	Semua	Sejak 2020
Kutipan	5861	2326
indeks-h	36	26
indeks-i10	86	56

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Mario OhlbergerProfessor for Applied Mathematics, University of Münster, GermanyEmail yang diverifikasi di Uni-Muenster.de
Gabriele SantinAssistant Professor, Ca' Foscari University of VeniceEmail yang diverifikasi di unive.it
Martin DrohmannEmail yang diverifikasi di sandia.gov
Markus DihlmannUniversity of StuttgartEmail yang diverifikasi di mathematik.uni-stuttgart.de
Hans BurkhardtProfessor of Computer Science, University of FreiburgEmail yang diverifikasi di informatik.uni-freiburg.de
Jörg FehrProfessor at Institute of Engineering and Computational Mechanics and SimTech, University ofEmail yang diverifikasi di itm.uni-stuttgart.de
Elżbieta PękalskaPhD, University of ManchesterEmail yang diverifikasi di cs.man.ac.uk
Gianluigi RozzaFull Professor of Numerical Analysis, SISSA, Int. School for Adv. Studies, Italy and ERC PIEmail yang diverifikasi di sissa.it
Claus BahlmannUniversity of Freiburg, Siemens Corporate Research, Siemens AG, Siemens MobilityEmail yang diverifikasi di siemens.com
Barbara WohlmuthProefssorin für Mathematik, TU MünchenEmail yang diverifikasi di ma.tum.de
julien salomonINRIA & Sorbonne UniversitéEmail yang diverifikasi di inria.fr
Peter EberhardProfessor, University of Stuttgart, GermanyEmail yang diverifikasi di itm.uni-stuttgart.de
Nils KarajanPostDoc for Continuum Biomechanics, SimTech Cluster of Excellence, University of StuttgartEmail yang diverifikasi di karajan.de
Daniel KeysersGoogleEmail yang diverifikasi di google.com
David AmsallemData Science Manager, FacebookEmail yang diverifikasi di fb.com
Danny SorensenRice UniversityEmail yang diverifikasi di rice.edu
Karsten UrbanUlm University, Institute for Numerical MathematicsEmail yang diverifikasi di uni-ulm.de
Christian RohdeUniversität StuttgartEmail yang diverifikasi di mathematik.uni-stuttgart.de
Martin Rumpfprofessor for mathematics, University of BonnEmail yang diverifikasi di uni-bonn.de
Peter BennerMax Planck Institute for Dynamics of Complex Technical SystemsEmail yang diverifikasi di mpi-magdeburg.mpg.de

Ikuti

Bernard Haasdonk

Professor for Numerical Mathematics

Email yang diverifikasi di mathematik.uni-stuttgart.de - Beranda

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


Judul Urutkan menurut kutipan Urutkan menurut tahun Urutkan menurut judul	Dikutip oleh Dikutip oleh	Tahun
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	575	2002
Reduced basis method for finite volume approximationsof parametrizedlinear evolution equations B Haasdonk, M Ohlberger ESAIM: Mathematical Modelling and Numerical Analysis 42 (2), 277-302, 2008	551	2008
Feature space interpretation of SVMs with indefinite kernels B Haasdonk IEEE Transactions on pattern analysis and machine intelligence 27 (4), 482-492, 2005	337	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	282	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	227	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	214	2017
Learning with distance substitution kernels B Haasdonk, C Bahlmann Joint pattern recognition symposium, 220-227, 2004	203	2004
Convergence rates of the pod–greedy method B Haasdonk ESAIM: Mathematical modelling and numerical Analysis 47 (3), 859-873, 2013	191	2013
Tangent distance kernels for support vector machines B Haasdonk, D Keysers 2002 International Conference on Pattern Recognition 2, 864-868, 2002	169	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	163	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	143	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	135	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	118	2011
Invariant kernel functions for pattern analysis and machine learning B Haasdonk, H Burkhardt Machine learning 68, 35-61, 2007	114	2007
A reduced basis method for evolution schemes with parameter-dependent explicit operators B Haasdonk, M Ohlberger, G Rozza	86	2008
Convergence rate of the data-independent -greedy algorithm in kernel-based approximation G Santin, B Haasdonk arXiv preprint arXiv:1612.02672, 2016	76	2016
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	72	2015
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	71*
Adaptive reduced basis methods for nonlinear convection–diffusion equations M Drohmann, B Haasdonk, M Ohlberger Finite Volumes for Complex Applications VI Problems & Perspectives: FVCA 6 …, 2011	63	2011
The localized reduced basis multiscale method F Albrecht, B Haasdonk, S Kaulmann, M Ohlberger	63*

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