Bernard Haasdonk

94 papers A* 1B 1Journal 73Unranked 18

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Analysis of structured deep kernel networks. J. Comput. Appl. Math. Tizian Wenzel, Gabriele Santin, Bernard Haasdonk
2026	J	`jnl`	Refined rates of convergence for target-data dependent greedy generalized interpolation with Sobolev kernels. CoRR Bernard Haasdonk, Gabriele Santin, Tizian Wenzel, Daniel Winkle
2026	J	`jnl`	Symplecticity-Preserving Prediction of Hamiltonian Dynamics by Generalized Kernel Interpolation. CoRR Robin Herkert, Tobias Ehring, Bernard Haasdonk
2025	J	`jnl`	A trust-region framework for optimization using Hermite kernel surrogate models. CoRR Sven Ullmann, Tobias Ehring, Robin Herkert, Bernard Haasdonk
2025	J	`jnl`	Escaping the native space of Sobolev kernels by interpolation. CoRR Tobias Ehring, Max-Paul Vogel, Bernard Haasdonk
2025	J	`jnl`	Kernel-based Greedy Approximation of Parametric Elliptic Boundary Value Problems. CoRR Bernard Haasdonk, Gabriele Santin, Tizian Wenzel
2025	J	`jnl`	On the Convergence of the Policy Iteration for Infinite-Horizon Nonlinear Optimal Control Problems. CoRR Tobias Ehring, Behzad Azmi, Bernard Haasdonk
2025	J	`jnl`	Recovery of the optimal control value function in reproducing kernel Hilbert spaces from verification conditions. CoRR Tobias Ehring, Behzad Azmi, Bernard Haasdonk
2025	J	`jnl`	Solving Approximation Tasks with Greedy Deep Kernel Methods. CoRR Marian Klink, Tobias Ehring, Robin Herkert, Robin Lautenschlager, Dominik Göddeke, Bernard Haasdonk
2024	J	`jnl`	Data-driven identification of latent port-Hamiltonian systems. CoRR Johannes Rettberg, Jonas Kneifl, Julius Herb, Patrick Buchfink, Jörg Fehr, Bernard Haasdonk
2024	J	`jnl`	Dictionary-based online-adaptive structure-preserving model order reduction for parametric Hamiltonian systems. Adv. Comput. Math. Robin Herkert, Patrick Buchfink, Bernard Haasdonk
2024	J	`jnl`	Error Analysis of Randomized Symplectic Model Order Reduction for Hamiltonian systems. CoRR Robin Herkert, Patrick Buchfink, Bernard Haasdonk, Johannes Rettberg, Jörg Fehr
2024	J	`jnl`	Greedy Kernel Methods for Approximating Breakthrough Curves for Reactive Flow from 3D Porous Geometry Data. CoRR Robin Herkert, Patrick Buchfink, Tizian Wenzel, Bernard Haasdonk, Pavel Toktaliev, Oleg Iliev
2024	J	`jnl`	Hermite kernel surrogates for the value function of high-dimensional nonlinear optimal control problems. Adv. Comput. Math. Tobias Ehring, Bernard Haasdonk
2024	J	`jnl`	Improved a posteriori error bounds for reduced port-Hamiltonian systems. Adv. Comput. Math. Johannes Rettberg, Dominik Wittwar, Patrick Buchfink, Robin Herkert, Jörg Fehr, Bernard Haasdonk
2024	J	`jnl`	On the Optimality of Target-Data-Dependent Kernel Greedy Interpolation in Sobolev Reproducing Kernel Hilbert Spaces. SIAM J. Numer. Anal. Gabriele Santin, Tizian Wenzel, Bernard Haasdonk
2023	J	`jnl`	A New Certified Hierarchical and Adaptive RB-ML-ROM Surrogate Model for Parametrized PDEs. SIAM J. Sci. Comput. Bernard Haasdonk, Hendrik Kleikamp, Mario Ohlberger, Felix Schindler, Tizian Wenzel
2023	—	`conf`	Application of Deep Kernel Models for Certified and Adaptive RB-ML-ROM Surrogate Modeling. LSSC Tizian Wenzel, Bernard Haasdonk, Hendrik Kleikamp, Mario Ohlberger, Felix Schindler
2023	J	`jnl`	Application of Deep Kernel Models for Certified and Adaptive RB-ML-ROM Surrogate Modeling. CoRR Tizian Wenzel, Bernard Haasdonk, Hendrik Kleikamp, Mario Ohlberger, Felix Schindler
2023	J	`jnl`	Approximation Bounds for Model Reduction on Polynomially Mapped Manifolds. CoRR Patrick Buchfink, Silke Glas, Bernard Haasdonk
2023	J	`jnl`	Dictionary-based Online-adaptive Structure-preserving Model Order Reduction for Parametric Hamiltonian Systems. CoRR Robin Herkert, Patrick Buchfink, Bernard Haasdonk
2023	J	`jnl`	Hermite kernel surrogates for the value function of high-dimensional nonlinear optimal control problems. CoRR Tobias Ehring, Bernard Haasdonk
2023	J	`jnl`	Improved a posteriori Error Bounds for Reduced port-Hamiltonian Systems. CoRR Johannes Rettberg, Dominik Wittwar, Patrick Buchfink, Robin Herkert, Jörg Fehr, Bernard Haasdonk
2023	J	`jnl`	Model Reduction on Manifolds: A differential geometric framework. CoRR Patrick Buchfink, Silke Glas, Bernard Haasdonk, Benjamin Unger
2023	J	`jnl`	On the optimality of target-data-dependent kernel greedy interpolation in Sobolev Reproducing Kernel Hilbert Spaces. CoRR Gabriele Santin, Tizian Wenzel, Bernard Haasdonk
2023	—	`conf`	Randomized Symplectic Model Order Reduction for Hamiltonian Systems. LSSC Robin Herkert, Patrick Buchfink, Bernard Haasdonk, Johannes Rettberg, Jörg Fehr
2023	J	`jnl`	Randomized Symplectic Model Order Reduction for Hamiltonian Systems. CoRR Robin Herkert, Patrick Buchfink, Bernard Haasdonk, Johannes Rettberg, Jörg Fehr
2023	J	`jnl`	Symplectic Model Reduction of Hamiltonian Systems on Nonlinear Manifolds and Approximation with Weakly Symplectic Autoencoder. SIAM J. Sci. Comput. Patrick Buchfink, Silke Glas, Bernard Haasdonk
2022	J	`jnl`	A new certified hierarchical and adaptive RB-ML-ROM surrogate model for parametrized PDEs. CoRR Bernard Haasdonk, Hendrik Kleikamp, Mario Ohlberger, Felix Schindler, Tizian Wenzel
2022	J	`jnl`	Adaptive meshfree solution of linear partial differential equations with PDE-greedy kernel methods. CoRR Tizian Wenzel, Daniel Winkle, Gabriele Santin, Bernard Haasdonk
2022	J	`jnl`	Port-Hamiltonian Fluid-Structure Interaction Modeling and Structure-Preserving Model Order Reduction of a Classical Guitar. CoRR Johannes Rettberg, Dominik Wittwar, Patrick Buchfink, Alexander Brauchler, Pascal Ziegler, Jörg Fehr, Bernard Haasdonk
2022	J	`jnl`	Stability of convergence rates: Kernel interpolation on non-Lipschitz domains. CoRR Tizian Wenzel, Gabriele Santin, Bernard Haasdonk
2022	—	`conf`	Surrogate-data-enriched Physics-Aware Neural Networks. NLDL Raphael Leiteritz, Patrick Buchfink, Bernard Haasdonk, Dirk Pflüger
2021	—	`conf`	A Full Order, Reduced Order and Machine Learning Model Pipeline for Efficient Prediction of Reactive Flows. LSSC Pavel Gavrilenko, Bernard Haasdonk, Oleg Iliev, Mario Ohlberger, Felix Schindler, Pavel Toktaliev, Tizian Wenzel, Maha Youssef
2021	J	`jnl`	A full order, reduced order and machine learning model pipeline for efficient prediction of reactive flows. CoRR Pavel Gavrilenko, Bernard Haasdonk, Oleg Iliev, Mario Ohlberger, Felix Schindler, Pavel Toktaliev, Tizian Wenzel, Maha Youssef
2021	J	`jnl`	A novel class of stabilized greedy kernel approximation algorithms: Convergence, stability and uniform point distribution. J. Approx. Theory Tizian Wenzel, Gabriele Santin, Bernard Haasdonk
2021	J	`jnl`	An adaptive model hierarchy for data-augmented training of kernel models for reactive flow. CoRR Bernard Haasdonk, Mario Ohlberger, Felix Schindler
2021	J	`jnl`	Analysis of target data-dependent greedy kernel algorithms: Convergence rates for f-, $f \cdot P$- and f/P-greedy. CoRR Tizian Wenzel, Gabriele Santin, Bernard Haasdonk
2021	—	`conf`	Reduced Basis Methods for Efficient Simulation of a Rigid Robot Hand Interacting with Soft Tissue. LSSC Shahnewaz Shuva, Patrick Buchfink, Oliver Röhrle, Bernard Haasdonk
2021	J	`jnl`	Reduced Basis Methods for Efficient Simulation of a Rigid Robot Hand Interacting with Soft Tissue. CoRR Shahnewaz Shuva, Patrick Buchfink, Oliver Röhrle, Bernard Haasdonk
2021	—	`conf`	Structured Deep Kernel Networks for Data-Driven Closure Terms of Turbulent Flows. LSSC Tizian Wenzel, Marius Kurz, Andrea Beck, Gabriele Santin, Bernard Haasdonk
2021	J	`jnl`	Structured Deep Kernel Networks for Data-Driven Closure Terms of Turbulent Flows. CoRR Tizian Wenzel, Marius Kurz, Andrea Beck, Gabriele Santin, Bernard Haasdonk
2021	J	`jnl`	Surrogate-data-enriched Physics-Aware Neural Networks. CoRR Raphael Leiteritz, Patrick Buchfink, Bernard Haasdonk, Dirk Pflüger
2021	J	`jnl`	Symplectic Model Reduction of Hamiltonian Systems on Nonlinear Manifolds. CoRR Patrick Buchfink, Silke Glas, Bernard Haasdonk
2021	J	`jnl`	Universality and Optimality of Structured Deep Kernel Networks. CoRR Tizian Wenzel, Gabriele Santin, Bernard Haasdonk
2020	J	`jnl`	Biomechanical surrogate modelling using stabilized vectorial greedy kernel methods. CoRR Bernard Haasdonk, Tizian Wenzel, Gabriele Santin, Syn Schmitt
2020	J	`jnl`	Feedback control of parametrized PDEs via model order reduction and dynamic programming principle. Adv. Comput. Math. Alessandro Alla, Bernard Haasdonk, Andreas Schmidt
2020	J	`jnl`	Kernel methods for center manifold approximation and a data-based version of the Center Manifold Theorem. CoRR Bernard Haasdonk, Boumediene Hamzi, Gabriele Santin, Dominik Wittwar
2020	J	`jnl`	Rigorous and effective a-posteriori error bounds for nonlinear problems - application to RB methods. Adv. Comput. Math. Andreas Schmidt, Dominik Wittwar, Bernard Haasdonk
2020	J	`jnl`	Sampling based approximation of linear functionals in Reproducing Kernel Hilbert Spaces. CoRR Gabriele Santin, Toni Karvonen, Bernard Haasdonk
2019	J	`jnl`	A novel class of stabilized greedy kernel approximation algorithms: Convergence, stability & uniform point distribution. CoRR Tizian Wenzel, Gabriele Santin, Bernard Haasdonk
2019	—	`conf`	Biomechanical Surrogate Modelling Using Stabilized Vectorial Greedy Kernel Methods. ENUMATH Bernard Haasdonk, Tizian Wenzel, Gabriele Santin, Syn Schmitt
2019	—	`conf`	Convergence Rates for Matrix P-Greedy Variants. ENUMATH Dominik Wittwar, Bernard Haasdonk
2019	J	`jnl`	Data-Driven Time Parallelism via Forecasting. SIAM J. Sci. Comput. Kevin Carlberg, Lukas Brencher, Bernard Haasdonk, Andrea Barth
2019	J	`jnl`	Deep recurrent Gaussian process with variational Sparse Spectrum approximation. CoRR Roman Föll, Bernard Haasdonk, Markus Hanselmann, Holger Ulmer
2019	—	`conf`	Experimental Comparison of Symplectic and Non-symplectic Model Order Reduction on an Uncertainty Quantification Problem. ENUMATH Patrick Buchfink, Bernard Haasdonk
2019	J	`jnl`	Kernel Methods for Surrogate Modeling. CoRR Gabriele Santin, Bernard Haasdonk
2018	J	`jnl`	Certified Reduced Basis Approximation for the Coupling of Viscous and Inviscid Parametrized Flow Models. J. Sci. Comput. Immanuel Martini, Bernard Haasdonk, Gianluigi Rozza
2018	J	`jnl`	Comparison of data-driven uncertainty quantification methods for a carbon dioxide storage benchmark scenario. CoRR Markus Köppel, Fabian Franzelin, Ilja Kröker, Sergey Oladyshkin, Gabriele Santin, Dominik Wittwar, Andrea Barth, Bernard Haasdonk, Wolfgang Nowak, Dirk Pflüger, Christian Rohde
2018	J	`jnl`	Enabling Interactive Mobile Simulations Through Distributed Reduced Models. CoRR Christoph Dibak, Bernard Haasdonk, Andreas Schmidt, Frank Dürr, Kurt Rothermel
2018	J	`jnl`	Enabling interactive mobile simulations through distributed reduced models. Pervasive Mob. Comput. Christoph Dibak, Bernard Haasdonk, Andreas Schmidt, Frank Dürr, Kurt Rothermel
2018	J	`jnl`	Numerical modelling of a peripheral arterial stenosis using dimensionally reduced models and machine learning techniques. CoRR Tobias Köppl, Gabriele Santin, Bernard Haasdonk, Rainer Helmig
2017	A*	`conf`	Server-assisted interactive mobile simulations for pervasive applications. PerCom Christoph Dibak, Andreas Schmidt, Frank Dürr, Bernard Haasdonk, Kurt Rothermel
2016	J	`jnl`	An algorithmic comparison of the Hyper-Reduction and the Discrete Empirical Interpolation Method for a nonlinear thermal problem. CoRR Felix Fritzen, Bernard Haasdonk, David Ryckelynck, Sebastian Schöps
2016	J	`jnl`	Data-driven time parallelism via forecasting. CoRR Kevin Carlberg, Lukas Brencher, Bernard Haasdonk, Andrea Barth
2016	J	`jnl`	PEBL-ROM: Projection-error based local reduced-order models. Adv. Model. Simul. Eng. Sci. David Amsallem, Bernard Haasdonk
2015	J	`jnl`	A POD-EIM reduced two-scale model for crystal growth. Adv. Comput. Math. Magnus Redeker, Bernard Haasdonk
2015	J	`jnl`	Certified PDE-constrained parameter optimization using reduced basis surrogate models for evolution problems. Comput. Optim. Appl. Markus A. Dihlmann, Bernard Haasdonk
2015	J	`jnl`	Reduced Basis Methods for Pricing Options with the Black-Scholes and Heston Models. SIAM J. Financial Math. Olena Burkovska, Bernard Haasdonk, Julien Salomon, Barbara I. Wohlmuth
2015	J	`jnl`	Reduced basis approximation and a-posteriori error estimation for the coupled Stokes-Darcy system. Adv. Comput. Math. Immanuel Martini, Gianluigi Rozza, Bernard Haasdonk
2014	J	`jnl`	A Posteriori Error Estimation for DEIM Reduced Nonlinear Dynamical Systems. SIAM J. Sci. Comput. Daniel Wirtz, Danny C. Sorensen, Bernard Haasdonk
2013	—	`conf`	Output Error Bounds for the Dirichlet-Neumann Reduced Basis Method. ENUMATH Immanuel Martini, Bernard Haasdonk
2013	J	`jnl`	Reduced Basis Methods for Parameterized Partial Differential Equations with Stochastic Influences Using the Karhunen-Loève Expansion. SIAM/ASA J. Uncertain. Quantification Bernard Haasdonk, Karsten Urban, Bernhard Wieland
2012	J	`jnl`	A Reduced Basis Method for Parametrized Variational Inequalities. SIAM J. Numer. Anal. Bernard Haasdonk, Julien Salomon, Barbara I. Wohlmuth
2012	J	`jnl`	Efficient a-posteriori error estimation for nonlinear kernel-based reduced systems. Syst. Control. Lett. Daniel Wirtz, Bernard Haasdonk
2012	J	`jnl`	Efficient parametric analysis of the chemical master equation through model order reduction. BMC Syst. Biol. Steffen Waldherr, Bernard Haasdonk
2012	J	`jnl`	Reduced Basis Approximation for Nonlinear Parametrized Evolution Equations based on Empirical Operator Interpolation. SIAM J. Sci. Comput. Martin Drohmann, Bernard Haasdonk, Mario Ohlberger
2010	J	`jnl`	Effiziente und gesicherte Modellreduktion für parametrisierte dynamische Systeme (Efficient and Certified Model Reduction for Parametrized Dynamical Systems). Autom. Bernard Haasdonk
2010	—	`conf`	Indefinite Kernel Discriminant Analysis. COMPSTAT Bernard Haasdonk, Elzbieta Pekalska
2009	J	`jnl`	Kernel Discriminant Analysis for Positive Definite and Indefinite Kernels. IEEE Trans. Pattern Anal. Mach. Intell. Elzbieta Pekalska, Bernard Haasdonk
2009	J	`jnl`	Reduced Basis Method for quadratically nonlinear transport equations. Int. J. Comput. Sci. Math. Nadine Jung, Bernard Haasdonk, Dietmar Kröner
2008	—	`conf`	Classification with Kernel Mahalanobis Distance Classifiers. GfKl Bernard Haasdonk, Elzbieta Pekalska
2008	B	`conf`	Indefinite Kernel Fisher Discriminant. ICPR Bernard Haasdonk, Elzbieta Pekalska
2007	—	`conf`	Classification with Invariant Distance Substitution Kernels. GfKl Bernard Haasdonk, Hans Burkhardt
2007	J	`jnl`	Invariant kernel functions for pattern analysis and machine learning. Mach. Learn. Bernard Haasdonk, Hans Burkhardt
2006	—	`—`	Transformation knowledge in pattern analysis with kernel methods: distance and integration kernels. Bernard Haasdonk
2005	J	`jnl`	Feature Space Interpretation of SVMs with Indefinite Kernels. IEEE Trans. Pattern Anal. Mach. Intell. Bernard Haasdonk
2005	—	`conf`	Invariance in Kernel Methods by Haar-Integration Kernels. SCIA Bernard Haasdonk, A. Vossen, Hans Burkhardt
2004	—	`conf`	Adjustable Invariant Features by Partial Haar-Integration. ICPR (2) Bernard Haasdonk, Alaa Halawani, Hans Burkhardt
2004	—	`conf`	Learning with Distance Substitution Kernels. DAGM-Symposium Bernard Haasdonk, Claus Bahlmann
2003	J	`jnl`	Multiresolution Visualization of Higher Order Adaptive Finite Element Simulations. Computing Bernard Haasdonk, Mario Ohlberger, Martin Rumpf, Alfred Schmidt, Kunibert G. Siebert
2002	—	`conf`	Online handwriting recognition with support vector machines - a kernel approach. IWFHR Claus Bahlmann, Bernard Haasdonk, Hans Burkhardt
2002	—	`conf`	Tangent Distance Kernels for Support Vector Machines. ICPR (2) Bernard Haasdonk, Daniel Keysers
2001	J	`jnl`	Convergence of a staggered Lax-Friedrichs scheme for nonlinear conservation laws on unstructured two-dimensional grids. Numerische Mathematik Bernard Haasdonk, Dietmar Kröner, Christian Rohde