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ORCIDBernard Haasdonk
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2020 – today
- 2024
[j30]Robin Herkert
, Patrick Buchfink, Bernard Haasdonk:
Dictionary-based online-adaptive structure-preserving model order reduction for parametric Hamiltonian systems. Adv. Comput. Math. 50(1): 12 (2024)- [j29]Tobias Ehring, Bernard Haasdonk:
Hermite kernel surrogates for the value function of high-dimensional nonlinear optimal control problems. Adv. Comput. Math. 50(3): 36 (2024) - [j28]Johannes Rettberg, Dominik Wittwar, Patrick Buchfink, Robin Herkert, Jörg Fehr, Bernard Haasdonk:
Improved a posteriori error bounds for reduced port-Hamiltonian systems. Adv. Comput. Math. 50(5): 100 (2024) - [j27]Gabriele Santin, Tizian Wenzel, Bernard Haasdonk:
On the Optimality of Target-Data-Dependent Kernel Greedy Interpolation in Sobolev Reproducing Kernel Hilbert Spaces. SIAM J. Numer. Anal. 62(5): 2249-2275 (2024) - [i34]Robin Herkert, Patrick Buchfink, Bernard Haasdonk, Johannes Rettberg, Jörg Fehr:
Error Analysis of Randomized Symplectic Model Order Reduction for Hamiltonian systems. CoRR abs/2405.10465 (2024) - [i33]Robin Herkert, Patrick Buchfink, Tizian Wenzel, Bernard Haasdonk, Pavel Toktaliev, Oleg Iliev:
Greedy Kernel Methods for Approximating Breakthrough Curves for Reactive Flow from 3D Porous Geometry Data. CoRR abs/2405.19170 (2024) - [i32]Johannes Rettberg, Jonas Kneifl, Julius Herb, Patrick Buchfink, Jörg Fehr, Bernard Haasdonk:
Data-driven identification of latent port-Hamiltonian systems. CoRR abs/2408.08185 (2024) - 2023
- [j26]Patrick Buchfink, Silke Glas, Bernard Haasdonk:
Symplectic Model Reduction of Hamiltonian Systems on Nonlinear Manifolds and Approximation with Weakly Symplectic Autoencoder. SIAM J. Sci. Comput. 45(2): 289- (2023) - [j25]Bernard Haasdonk, Hendrik Kleikamp, Mario Ohlberger, Felix Schindler, Tizian Wenzel:
A New Certified Hierarchical and Adaptive RB-ML-ROM Surrogate Model for Parametrized PDEs. SIAM J. Sci. Comput. 45(3): 1039-1065 (2023) - [c20]Robin Herkert, Patrick Buchfink, Bernard Haasdonk, Johannes Rettberg, Jörg Fehr:
Randomized Symplectic Model Order Reduction for Hamiltonian Systems. LSSC 2023: 99-107 - [c19]Tizian Wenzel, Bernard Haasdonk, Hendrik Kleikamp, Mario Ohlberger, Felix Schindler:
Application of Deep Kernel Models for Certified and Adaptive RB-ML-ROM Surrogate Modeling. LSSC 2023: 117-125 - [i31]Tizian Wenzel, Bernard Haasdonk, Hendrik Kleikamp, Mario Ohlberger, Felix Schindler:
Application of Deep Kernel Models for Certified and Adaptive RB-ML-ROM Surrogate Modeling. CoRR abs/2302.14526 (2023) - [i30]Robin Herkert, Patrick Buchfink, Bernard Haasdonk, Johannes Rettberg, Jörg Fehr:
Randomized Symplectic Model Order Reduction for Hamiltonian Systems. CoRR abs/2303.04036 (2023) - [i29]Johannes Rettberg, Dominik Wittwar, Patrick Buchfink, Robin Herkert, Jörg Fehr, Bernard Haasdonk:
Improved a posteriori Error Bounds for Reduced port-Hamiltonian Systems. CoRR abs/2303.17329 (2023) - [i28]Robin Herkert, Patrick Buchfink, Bernard Haasdonk:
Dictionary-based Online-adaptive Structure-preserving Model Order Reduction for Parametric Hamiltonian Systems. CoRR abs/2303.18072 (2023) - [i27]Tobias Ehring, Bernard Haasdonk:
Hermite kernel surrogates for the value function of high-dimensional nonlinear optimal control problems. CoRR abs/2305.06122 (2023) - [i26]Gabriele Santin, Tizian Wenzel, Bernard Haasdonk:
On the optimality of target-data-dependent kernel greedy interpolation in Sobolev Reproducing Kernel Hilbert Spaces. CoRR abs/2307.09811 (2023) - [i25]Patrick Buchfink, Silke Glas, Bernard Haasdonk:
Approximation Bounds for Model Reduction on Polynomially Mapped Manifolds. CoRR abs/2312.00724 (2023) - [i24]Patrick Buchfink, Silke Glas, Bernard Haasdonk, Benjamin Unger:
Model Reduction on Manifolds: A differential geometric framework. CoRR abs/2312.01963 (2023) - 2022
- [c18]Raphael Leiteritz, Patrick Buchfink, Bernard Haasdonk, Dirk Pflüger:
Surrogate-data-enriched Physics-Aware Neural Networks. NLDL 2022 - [i23]Johannes Rettberg, Dominik Wittwar, Patrick Buchfink, Alexander Brauchler, Pascal Ziegler, Jörg Fehr, Bernard Haasdonk:
Port-Hamiltonian Fluid-Structure Interaction Modeling and Structure-Preserving Model Order Reduction of a Classical Guitar. CoRR abs/2203.10061 (2022) - [i22]Tizian Wenzel, Gabriele Santin, Bernard Haasdonk:
Stability of convergence rates: Kernel interpolation on non-Lipschitz domains. CoRR abs/2203.12532 (2022) - [i21]Bernard Haasdonk, Hendrik Kleikamp, Mario Ohlberger, Felix Schindler, Tizian Wenzel:
A new certified hierarchical and adaptive RB-ML-ROM surrogate model for parametrized PDEs. CoRR abs/2204.13454 (2022) - [i20]Tizian Wenzel, Daniel Winkle, Gabriele Santin, Bernard Haasdonk:
Adaptive meshfree solution of linear partial differential equations with PDE-greedy kernel methods. CoRR abs/2207.13971 (2022) - 2021
- [j24]Tizian Wenzel, Gabriele Santin, Bernard Haasdonk:
A novel class of stabilized greedy kernel approximation algorithms: Convergence, stability and uniform point distribution. J. Approx. Theory 262: 105508 (2021) - [c17]Pavel Gavrilenko, Bernard Haasdonk, Oleg Iliev, Mario Ohlberger, Felix Schindler, Pavel Toktaliev, Tizian Wenzel, Maha Youssef:
A Full Order, Reduced Order and Machine Learning Model Pipeline for Efficient Prediction of Reactive Flows. LSSC 2021: 378-386 - [c16]Shahnewaz Shuva, Patrick Buchfink, Oliver Röhrle, Bernard Haasdonk:
Reduced Basis Methods for Efficient Simulation of a Rigid Robot Hand Interacting with Soft Tissue. LSSC 2021: 402-409 - [c15]Tizian Wenzel, Marius Kurz, Andrea Beck, Gabriele Santin, Bernard Haasdonk:
Structured Deep Kernel Networks for Data-Driven Closure Terms of Turbulent Flows. LSSC 2021: 410-418 - [i19]Tizian Wenzel, Marius Kurz, Andrea Beck, Gabriele Santin, Bernard Haasdonk:
Structured Deep Kernel Networks for Data-Driven Closure Terms of Turbulent Flows. CoRR abs/2103.13655 (2021) - [i18]Shahnewaz Shuva, Patrick Buchfink, Oliver Röhrle, Bernard Haasdonk:
Reduced Basis Methods for Efficient Simulation of a Rigid Robot Hand Interacting with Soft Tissue. CoRR abs/2103.15422 (2021) - [i17]Pavel Gavrilenko, Bernard Haasdonk, Oleg Iliev, Mario Ohlberger, Felix Schindler, Pavel Toktaliev, Tizian Wenzel, Maha Youssef:
A full order, reduced order and machine learning model pipeline for efficient prediction of reactive flows. CoRR abs/2104.02800 (2021) - [i16]Tizian Wenzel, Gabriele Santin, Bernard Haasdonk:
Universality and Optimality of Structured Deep Kernel Networks. CoRR abs/2105.07228 (2021) - [i15]Tizian Wenzel, Gabriele Santin, Bernard Haasdonk:
Analysis of target data-dependent greedy kernel algorithms: Convergence rates for f-, $f \cdot P$- and f/P-greedy. CoRR abs/2105.07411 (2021) - [i14]Bernard Haasdonk, Mario Ohlberger, Felix Schindler:
An adaptive model hierarchy for data-augmented training of kernel models for reactive flow. CoRR abs/2110.12388 (2021) - [i13]Raphael Leiteritz, Patrick Buchfink, Bernard Haasdonk, Dirk Pflüger:
Surrogate-data-enriched Physics-Aware Neural Networks. CoRR abs/2112.05489 (2021) - [i12]Patrick Buchfink, Silke Glas, Bernard Haasdonk:
Symplectic Model Reduction of Hamiltonian Systems on Nonlinear Manifolds. CoRR abs/2112.10815 (2021) - 2020
- [j23]Alessandro Alla, Bernard Haasdonk, Andreas Schmidt:
Feedback control of parametrized PDEs via model order reduction and dynamic programming principle. Adv. Comput. Math. 46(1): 9 (2020) - [j22]Andreas Schmidt, Dominik Wittwar, Bernard Haasdonk:
Rigorous and effective a-posteriori error bounds for nonlinear problems - application to RB methods. Adv. Comput. Math. 46(2): 32 (2020) - [i11]Gabriele Santin, Toni Karvonen, Bernard Haasdonk:
Sampling based approximation of linear functionals in Reproducing Kernel Hilbert Spaces. CoRR abs/2004.00556 (2020) - [i10]Bernard Haasdonk, Tizian Wenzel, Gabriele Santin, Syn Schmitt:
Biomechanical surrogate modelling using stabilized vectorial greedy kernel methods. CoRR abs/2004.12670 (2020) - [i9]Bernard Haasdonk, Boumediene Hamzi, Gabriele Santin, Dominik Wittwar:
Kernel methods for center manifold approximation and a data-based version of the Center Manifold Theorem. CoRR abs/2012.00338 (2020)
2010 – 2019
- 2019
- [j21]Kevin Carlberg, Lukas Brencher, Bernard Haasdonk, Andrea Barth:
Data-Driven Time Parallelism via Forecasting. SIAM J. Sci. Comput. 41(3): B466-B496 (2019) - [c14]Patrick Buchfink, Bernard Haasdonk:
Experimental Comparison of Symplectic and Non-symplectic Model Order Reduction on an Uncertainty Quantification Problem. ENUMATH 2019: 205-213 - [c13]Bernard Haasdonk, Tizian Wenzel, Gabriele Santin, Syn Schmitt:
Biomechanical Surrogate Modelling Using Stabilized Vectorial Greedy Kernel Methods. ENUMATH 2019: 499-508 - [c12]Dominik Wittwar, Bernard Haasdonk:
Convergence Rates for Matrix P-Greedy Variants. ENUMATH 2019: 1195-1203 - [i8]Gabriele Santin, Bernard Haasdonk:
Kernel Methods for Surrogate Modeling. CoRR abs/1907.10556 (2019) - [i7]Roman Föll, Bernard Haasdonk, Markus Hanselmann, Holger Ulmer:
Deep recurrent Gaussian process with variational Sparse Spectrum approximation. CoRR abs/1909.13743 (2019) - [i6]Tizian Wenzel, Gabriele Santin, Bernard Haasdonk:
A novel class of stabilized greedy kernel approximation algorithms: Convergence, stability & uniform point distribution. CoRR abs/1911.04352 (2019) - 2018
- [j20]Immanuel Martini, Bernard Haasdonk, Gianluigi Rozza:
Certified Reduced Basis Approximation for the Coupling of Viscous and Inviscid Parametrized Flow Models. J. Sci. Comput. 74(1): 197-219 (2018) - [j19]Christoph Dibak, Bernard Haasdonk, Andreas Schmidt, Frank Dürr, Kurt Rothermel:
Enabling interactive mobile simulations through distributed reduced models. Pervasive Mob. Comput. 45: 19-34 (2018) - [i5]Markus Köppel, Fabian Franzelin, Ilja Kröker, Sergey Oladyshkin, Gabriele Santin, Dominik Wittwar, Andrea Barth, Bernard Haasdonk, Wolfgang Nowak, Dirk Pflüger, Christian Rohde:
Comparison of data-driven uncertainty quantification methods for a carbon dioxide storage benchmark scenario. CoRR abs/1802.03064 (2018) - [i4]Tobias Köppl, Gabriele Santin, Bernard Haasdonk, Rainer Helmig:
Numerical modelling of a peripheral arterial stenosis using dimensionally reduced models and machine learning techniques. CoRR abs/1802.04628 (2018) - [i3]Christoph Dibak, Bernard Haasdonk, Andreas Schmidt, Frank Dürr, Kurt Rothermel:
Enabling Interactive Mobile Simulations Through Distributed Reduced Models. CoRR abs/1802.05206 (2018) - 2017
- [c11]Christoph Dibak, Andreas Schmidt, Frank Dürr, Bernard Haasdonk, Kurt Rothermel:
Server-assisted interactive mobile simulations for pervasive applications. PerCom 2017: 111-120 - 2016
- [j18]David Amsallem, Bernard Haasdonk:
PEBL-ROM: Projection-error based local reduced-order models. Adv. Model. Simul. Eng. Sci. 3(1): 6:1-6:25 (2016) - [i2]Felix Fritzen, Bernard Haasdonk, David Ryckelynck, Sebastian Schöps:
An algorithmic comparison of the Hyper-Reduction and the Discrete Empirical Interpolation Method for a nonlinear thermal problem. CoRR abs/1610.05029 (2016) - [i1]Kevin Carlberg, Lukas Brencher, Bernard Haasdonk, Andrea Barth:
Data-driven time parallelism via forecasting. CoRR abs/1610.09049 (2016) - 2015
- [j17]Magnus Redeker, Bernard Haasdonk:
A POD-EIM reduced two-scale model for crystal growth. Adv. Comput. Math. 41(5): 987-1013 (2015) - [j16]Immanuel Martini, Gianluigi Rozza, Bernard Haasdonk:
Reduced basis approximation and a-posteriori error estimation for the coupled Stokes-Darcy system. Adv. Comput. Math. 41(5): 1131-1157 (2015) - [j15]Markus A. Dihlmann, Bernard Haasdonk:
Certified PDE-constrained parameter optimization using reduced basis surrogate models for evolution problems. Comput. Optim. Appl. 60(3): 753-787 (2015) - [j14]Olena Burkovska, Bernard Haasdonk, Julien Salomon, Barbara I. Wohlmuth:
Reduced Basis Methods for Pricing Options with the Black-Scholes and Heston Models. SIAM J. Financial Math. 6(1): 685-712 (2015) - 2014
- [j13]Daniel Wirtz, Danny C. Sorensen, Bernard Haasdonk:
A Posteriori Error Estimation for DEIM Reduced Nonlinear Dynamical Systems. SIAM J. Sci. Comput. 36(2) (2014) - 2013
- [j12]Bernard Haasdonk, Karsten Urban, Bernhard Wieland:
Reduced Basis Methods for Parameterized Partial Differential Equations with Stochastic Influences Using the Karhunen-Loève Expansion. SIAM/ASA J. Uncertain. Quantification 1(1): 79-105 (2013) - [c10]Immanuel Martini, Bernard Haasdonk:
Output Error Bounds for the Dirichlet-Neumann Reduced Basis Method. ENUMATH 2013: 437-445 - 2012
- [j11]Steffen Waldherr, Bernard Haasdonk:
Efficient parametric analysis of the chemical master equation through model order reduction. BMC Syst. Biol. 6: 81 (2012) - [j10]Daniel Wirtz, Bernard Haasdonk:
Efficient a-posteriori error estimation for nonlinear kernel-based reduced systems. Syst. Control. Lett. 61(1): 203-211 (2012) - [j9]Bernard Haasdonk, Julien Salomon, Barbara I. Wohlmuth:
A Reduced Basis Method for Parametrized Variational Inequalities. SIAM J. Numer. Anal. 50(5): 2656-2676 (2012) - [j8]Martin Drohmann, Bernard Haasdonk, Mario Ohlberger:
Reduced Basis Approximation for Nonlinear Parametrized Evolution Equations based on Empirical Operator Interpolation. SIAM J. Sci. Comput. 34(2) (2012) - 2010
- [j7]Bernard Haasdonk:
Effiziente und gesicherte Modellreduktion für parametrisierte dynamische Systeme (Efficient and Certified Model Reduction for Parametrized Dynamical Systems). Autom. 58(8): 468-474 (2010) - [c9]Bernard Haasdonk, Elzbieta Pekalska:
Indefinite Kernel Discriminant Analysis. COMPSTAT 2010: 221-230
2000 – 2009
- 2009
- [j6]Nadine Jung, Bernard Haasdonk, Dietmar Kröner:
Reduced Basis Method for quadratically nonlinear transport equations. Int. J. Comput. Sci. Math. 2(4): 334-353 (2009) - [j5]Elzbieta Pekalska, Bernard Haasdonk:
Kernel Discriminant Analysis for Positive Definite and Indefinite Kernels. IEEE Trans. Pattern Anal. Mach. Intell. 31(6): 1017-1032 (2009) - 2008
- [c8]Bernard Haasdonk, Elzbieta Pekalska:
Classification with Kernel Mahalanobis Distance Classifiers. GfKl 2008: 351-361 - [c7]Bernard Haasdonk, Elzbieta Pekalska:
Indefinite Kernel Fisher Discriminant. ICPR 2008: 1-4 - 2007
- [j4]Bernard Haasdonk, Hans Burkhardt:
Invariant kernel functions for pattern analysis and machine learning. Mach. Learn. 68(1): 35-61 (2007) - [c6]Bernard Haasdonk, Hans Burkhardt:
Classification with Invariant Distance Substitution Kernels. GfKl 2007: 37-44 - 2006
- [b1]Bernard Haasdonk:
Transformation knowledge in pattern analysis with kernel methods: distance and integration kernels. University of Freiburg, Freiburg im Breisgau, Germany, Shaker 2006, ISBN 978-3-8322-5026-3, pp. 1-151 - 2005
- [j3]Bernard Haasdonk:
Feature Space Interpretation of SVMs with Indefinite Kernels. IEEE Trans. Pattern Anal. Mach. Intell. 27(4): 482-492 (2005) - [c5]Bernard Haasdonk, A. Vossen, Hans Burkhardt:
Invariance in Kernel Methods by Haar-Integration Kernels. SCIA 2005: 841-851 - 2004
- [c4]Bernard Haasdonk, Claus Bahlmann:
Learning with Distance Substitution Kernels. DAGM-Symposium 2004: 220-227 - [c3]Bernard Haasdonk, Alaa Halawani, Hans Burkhardt:
Adjustable Invariant Features by Partial Haar-Integration. ICPR (2) 2004: 769-774 - 2003
- [j2]Bernard Haasdonk, Mario Ohlberger, Martin Rumpf, Alfred Schmidt, Kunibert G. Siebert:
Multiresolution Visualization of Higher Order Adaptive Finite Element Simulations. Computing 70(3): 181-204 (2003) - 2002
- [c2]Claus Bahlmann, Bernard Haasdonk, Hans Burkhardt:
Online handwriting recognition with support vector machines - a kernel approach. IWFHR 2002: 49-54 - [c1]Bernard Haasdonk, Daniel Keysers:
Tangent Distance Kernels for Support Vector Machines. ICPR (2) 2002: 864-868 - 2001
- [j1]Bernard Haasdonk, Dietmar Kröner, Christian Rohde:
Convergence of a staggered Lax-Friedrichs scheme for nonlinear conservation laws on unstructured two-dimensional grids. Numerische Mathematik 88(3): 459-484 (2001)
Coauthor Index
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