Bernard Haasdonk

According to our database, Bernard Haasdonk authored at least 71 papers between 2001 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Improved a posteriori error bounds for reduced port-Hamiltonian systems.

Johannes Rettberg, Dominik Wittwar, Patrick Buchfink, Robin Herkert, Jörg Fehr, Bernard Haasdonk

Adv. Comput. Math., October, 2024

Hermite kernel surrogates for the value function of high-dimensional nonlinear optimal control problems.

Tobias Ehring, Bernard Haasdonk

Adv. Comput. Math., June, 2024

Dictionary-based online-adaptive structure-preserving model order reduction for parametric Hamiltonian systems.

Robin Herkert, Patrick Buchfink, Bernard Haasdonk

Adv. Comput. Math., February, 2024

On the Optimality of Target-Data-Dependent Kernel Greedy Interpolation in Sobolev Reproducing Kernel Hilbert Spaces.

Gabriele Santin, Tizian Wenzel, Bernard Haasdonk

SIAM J. Numer. Anal., 2024

Data-driven identification of latent port-Hamiltonian systems.

Johannes Rettberg, Jonas Kneifl, Julius Herb, Patrick Buchfink, Jörg Fehr, Bernard Haasdonk

CoRR, 2024

Greedy Kernel Methods for Approximating Breakthrough Curves for Reactive Flow from 3D Porous Geometry Data.

Robin Herkert, Patrick Buchfink, Tizian Wenzel, Bernard Haasdonk, Pavel Toktaliev, Oleg Iliev

CoRR, 2024

Error Analysis of Randomized Symplectic Model Order Reduction for Hamiltonian systems.

Robin Herkert, Patrick Buchfink, Bernard Haasdonk, Johannes Rettberg, Jörg Fehr

CoRR, 2024

2023

A New Certified Hierarchical and Adaptive RB-ML-ROM Surrogate Model for Parametrized PDEs.

Bernard Haasdonk, Hendrik Kleikamp, Mario Ohlberger, Felix Schindler, Tizian Wenzel

SIAM J. Sci. Comput., June, 2023

Symplectic Model Reduction of Hamiltonian Systems on Nonlinear Manifolds and Approximation with Weakly Symplectic Autoencoder.

Patrick Buchfink, Silke Glas, Bernard Haasdonk

SIAM J. Sci. Comput., April, 2023

Model Reduction on Manifolds: A differential geometric framework.

Patrick Buchfink, Silke Glas, Bernard Haasdonk, Benjamin Unger

CoRR, 2023

Approximation Bounds for Model Reduction on Polynomially Mapped Manifolds.

Patrick Buchfink, Silke Glas, Bernard Haasdonk

CoRR, 2023

Application of Deep Kernel Models for Certified and Adaptive RB-ML-ROM Surrogate Modeling.

Tizian Wenzel, Bernard Haasdonk, Hendrik Kleikamp, Mario Ohlberger, Felix Schindler

Proceedings of the Large-Scale Scientific Computations - 14th International Conference, 2023

Randomized Symplectic Model Order Reduction for Hamiltonian Systems.

Robin Herkert, Patrick Buchfink, Bernard Haasdonk, Johannes Rettberg, Jörg Fehr

Proceedings of the Large-Scale Scientific Computations - 14th International Conference, 2023

2022

Adaptive meshfree solution of linear partial differential equations with PDE-greedy kernel methods.

Tizian Wenzel, Daniel Winkle, Gabriele Santin, Bernard Haasdonk

CoRR, 2022

Stability of convergence rates: Kernel interpolation on non-Lipschitz domains.

Tizian Wenzel, Gabriele Santin, Bernard Haasdonk

CoRR, 2022

Port-Hamiltonian Fluid-Structure Interaction Modeling and Structure-Preserving Model Order Reduction of a Classical Guitar.

Johannes Rettberg, Dominik Wittwar, Patrick Buchfink, Alexander Brauchler, Pascal Ziegler, Jörg Fehr, Bernard Haasdonk

CoRR, 2022

Surrogate-data-enriched Physics-Aware Neural Networks.

Raphael Leiteritz, Patrick Buchfink, Bernard Haasdonk, Dirk Pflüger

Proceedings of the 2022 Northern Lights Deep Learning Workshop, 2022

2021

A novel class of stabilized greedy kernel approximation algorithms: Convergence, stability and uniform point distribution.

Tizian Wenzel, Gabriele Santin, Bernard Haasdonk

J. Approx. Theory, 2021

Symplectic Model Reduction of Hamiltonian Systems on Nonlinear Manifolds.

Patrick Buchfink, Silke Glas, Bernard Haasdonk

CoRR, 2021

An adaptive model hierarchy for data-augmented training of kernel models for reactive flow.

Bernard Haasdonk, Mario Ohlberger, Felix Schindler

CoRR, 2021

Analysis of target data-dependent greedy kernel algorithms: Convergence rates for f-, $f \cdot P$- and f/P-greedy.

Tizian Wenzel, Gabriele Santin, Bernard Haasdonk

CoRR, 2021

Universality and Optimality of Structured Deep Kernel Networks.

Tizian Wenzel, Gabriele Santin, Bernard Haasdonk

CoRR, 2021

Structured Deep Kernel Networks for Data-Driven Closure Terms of Turbulent Flows.

Tizian Wenzel, Marius Kurz, Andrea Beck, Gabriele Santin, Bernard Haasdonk

Proceedings of the Large-Scale Scientific Computing - 13th International Conference, 2021

Reduced Basis Methods for Efficient Simulation of a Rigid Robot Hand Interacting with Soft Tissue.

Shahnewaz Shuva, Patrick Buchfink, Oliver Röhrle, Bernard Haasdonk

Proceedings of the Large-Scale Scientific Computing - 13th International Conference, 2021

A Full Order, Reduced Order and Machine Learning Model Pipeline for Efficient Prediction of Reactive Flows.

Pavel Gavrilenko, Bernard Haasdonk, Oleg Iliev, Mario Ohlberger, Felix Schindler, Pavel Toktaliev, Tizian Wenzel, Maha Youssef

Proceedings of the Large-Scale Scientific Computing - 13th International Conference, 2021

2020

Kernel methods for center manifold approximation and a data-based version of the Center Manifold Theorem.

Bernard Haasdonk, Boumediene Hamzi, Gabriele Santin, Dominik Wittwar

CoRR, 2020

Sampling based approximation of linear functionals in Reproducing Kernel Hilbert Spaces.

Gabriele Santin, Toni Karvonen, Bernard Haasdonk

CoRR, 2020

Rigorous and effective a-posteriori error bounds for nonlinear problems - application to RB methods.

Andreas Schmidt, Dominik Wittwar, Bernard Haasdonk

Adv. Comput. Math., 2020

Feedback control of parametrized PDEs via model order reduction and dynamic programming principle.

Alessandro Alla, Bernard Haasdonk, Andreas Schmidt

Adv. Comput. Math., 2020

2019

Data-Driven Time Parallelism via Forecasting.

Kevin Carlberg, Lukas Brencher, Bernard Haasdonk, Andrea Barth

SIAM J. Sci. Comput., 2019

A novel class of stabilized greedy kernel approximation algorithms: Convergence, stability & uniform point distribution.

Tizian Wenzel, Gabriele Santin, Bernard Haasdonk

CoRR, 2019

Deep recurrent Gaussian process with variational Sparse Spectrum approximation.

Roman Föll, Bernard Haasdonk, Markus Hanselmann, Holger Ulmer

CoRR, 2019

Kernel Methods for Surrogate Modeling.

Gabriele Santin, Bernard Haasdonk

CoRR, 2019

Convergence Rates for Matrix P-Greedy Variants.

Dominik Wittwar, Bernard Haasdonk

Proceedings of the Numerical Mathematics and Advanced Applications ENUMATH 2019 - European Conference, Egmond aan Zee, The Netherlands, September 30, 2019

Biomechanical Surrogate Modelling Using Stabilized Vectorial Greedy Kernel Methods.

Bernard Haasdonk, Tizian Wenzel, Gabriele Santin, Syn Schmitt

Proceedings of the Numerical Mathematics and Advanced Applications ENUMATH 2019 - European Conference, Egmond aan Zee, The Netherlands, September 30, 2019

Experimental Comparison of Symplectic and Non-symplectic Model Order Reduction on an Uncertainty Quantification Problem.

Patrick Buchfink, Bernard Haasdonk

Proceedings of the Numerical Mathematics and Advanced Applications ENUMATH 2019 - European Conference, Egmond aan Zee, The Netherlands, September 30, 2019

2018

Enabling interactive mobile simulations through distributed reduced models.

Christoph Dibak, Bernard Haasdonk, Andreas Schmidt, Frank Dürr, Kurt Rothermel

Pervasive Mob. Comput., 2018

Certified Reduced Basis Approximation for the Coupling of Viscous and Inviscid Parametrized Flow Models.

Immanuel Martini, Bernard Haasdonk, Gianluigi Rozza

J. Sci. Comput., 2018

Numerical modelling of a peripheral arterial stenosis using dimensionally reduced models and machine learning techniques.

Tobias Köppl, Gabriele Santin, Bernard Haasdonk, Rainer Helmig

CoRR, 2018

Comparison of data-driven uncertainty quantification methods for a carbon dioxide storage benchmark scenario.

Markus Köppel, Fabian Franzelin, Ilja Kröker, Sergey Oladyshkin, Gabriele Santin, Dominik Wittwar, Andrea Barth, Bernard Haasdonk, Wolfgang Nowak, Dirk Pflüger, Christian Rohde

CoRR, 2018

2017

Server-assisted interactive mobile simulations for pervasive applications.

Christoph Dibak, Andreas Schmidt, Frank Dürr, Bernard Haasdonk, Kurt Rothermel

Proceedings of the 2017 IEEE International Conference on Pervasive Computing and Communications, 2017

2016

An algorithmic comparison of the Hyper-Reduction and the Discrete Empirical Interpolation Method for a nonlinear thermal problem.

Felix Fritzen, Bernard Haasdonk, David Ryckelynck, Sebastian Schöps

CoRR, 2016

PEBL-ROM: Projection-error based local reduced-order models.

David Amsallem, Bernard Haasdonk

Adv. Model. Simul. Eng. Sci., 2016

2015

Reduced Basis Methods for Pricing Options with the Black-Scholes and Heston Models.

Olena Burkovska, Bernard Haasdonk, Julien Salomon, Barbara I. Wohlmuth

SIAM J. Financial Math., 2015

Certified PDE-constrained parameter optimization using reduced basis surrogate models for evolution problems.

Markus A. Dihlmann, Bernard Haasdonk

Comput. Optim. Appl., 2015

A POD-EIM reduced two-scale model for crystal growth.

Magnus Redeker, Bernard Haasdonk

Adv. Comput. Math., 2015

Reduced basis approximation and a-posteriori error estimation for the coupled Stokes-Darcy system.

Immanuel Martini, Gianluigi Rozza, Bernard Haasdonk

Adv. Comput. Math., 2015

2014

A Posteriori Error Estimation for DEIM Reduced Nonlinear Dynamical Systems.

Daniel Wirtz, Danny C. Sorensen, Bernard Haasdonk

SIAM J. Sci. Comput., 2014

2013

Reduced Basis Methods for Parameterized Partial Differential Equations with Stochastic Influences Using the Karhunen-Loève Expansion.

Bernard Haasdonk, Karsten Urban, Bernhard Wieland

SIAM/ASA J. Uncertain. Quantification, 2013

Output Error Bounds for the Dirichlet-Neumann Reduced Basis Method.

Immanuel Martini, Bernard Haasdonk

Proceedings of the Numerical Mathematics and Advanced Applications - ENUMATH 2013, 2013

2012

Reduced Basis Approximation for Nonlinear Parametrized Evolution Equations based on Empirical Operator Interpolation.

Martin Drohmann, Bernard Haasdonk, Mario Ohlberger

SIAM J. Sci. Comput., 2012

A Reduced Basis Method for Parametrized Variational Inequalities.

Bernard Haasdonk, Julien Salomon, Barbara I. Wohlmuth

SIAM J. Numer. Anal., 2012

Efficient a-posteriori error estimation for nonlinear kernel-based reduced systems.

Daniel Wirtz, Bernard Haasdonk

Syst. Control. Lett., 2012

Efficient parametric analysis of the chemical master equation through model order reduction.

Steffen Waldherr, Bernard Haasdonk

BMC Syst. Biol., 2012

2010

Effiziente und gesicherte Modellreduktion für parametrisierte dynamische Systeme (Efficient and Certified Model Reduction for Parametrized Dynamical Systems).

Bernard Haasdonk

Autom., 2010

Indefinite Kernel Discriminant Analysis.

Bernard Haasdonk, Elzbieta Pekalska

Proceedings of the 19th International Conference on Computational Statistics, 2010

2009

Kernel Discriminant Analysis for Positive Definite and Indefinite Kernels.

Elzbieta Pekalska, Bernard Haasdonk

IEEE Trans. Pattern Anal. Mach. Intell., 2009

Reduced Basis Method for quadratically nonlinear transport equations.

Nadine Jung, Bernard Haasdonk, Dietmar Kröner

Int. J. Comput. Sci. Math., 2009

2008

Indefinite Kernel Fisher Discriminant.

Bernard Haasdonk, Elzbieta Pekalska

Proceedings of the 19th International Conference on Pattern Recognition (ICPR 2008), 2008

Classification with Kernel Mahalanobis Distance Classifiers.

Bernard Haasdonk, Elzbieta Pekalska

Proceedings of the Advances in Data Analysis, Data Handling and Business Intelligence, 2008

2007

Invariant kernel functions for pattern analysis and machine learning.

Bernard Haasdonk, Hans Burkhardt

Mach. Learn., 2007

Classification with Invariant Distance Substitution Kernels.

Bernard Haasdonk, Hans Burkhardt

Proceedings of the Data Analysis, Machine Learning and Applications, 2007

2006

Transformation knowledge in pattern analysis with kernel methods: distance and integration kernels.

Bernard Haasdonk

PhD thesis, 2006

2005

Feature Space Interpretation of SVMs with Indefinite Kernels.

Bernard Haasdonk

IEEE Trans. Pattern Anal. Mach. Intell., 2005

Invariance in Kernel Methods by Haar-Integration Kernels.

Bernard Haasdonk, A. Vossen, Hans Burkhardt

Proceedings of the Image Analysis, 14th Scandinavian Conference, 2005

2004

Adjustable Invariant Features by Partial Haar-Integration.

Bernard Haasdonk, Alaa Halawani, Hans Burkhardt

Proceedings of the 17th International Conference on Pattern Recognition, 2004

Learning with Distance Substitution Kernels.

Bernard Haasdonk, Claus Bahlmann

Proceedings of the Pattern Recognition, 26th DAGM Symposium, August 30, 2004

2003

Multiresolution Visualization of Higher Order Adaptive Finite Element Simulations.

Bernard Haasdonk, Mario Ohlberger, Martin Rumpf, Alfred Schmidt, Kunibert G. Siebert

Computing, 2003

2002

Tangent Distance Kernels for Support Vector Machines.

Bernard Haasdonk, Daniel Keysers

Proceedings of the 16th International Conference on Pattern Recognition, 2002

Online handwriting recognition with support vector machines - a kernel approach.

Claus Bahlmann, Bernard Haasdonk, Hans Burkhardt

Proceedings of the Eighth International Workshop on Frontiers in Handwriting Recognition, 2002

2001

Convergence of a staggered Lax-Friedrichs scheme for nonlinear conservation laws on unstructured two-dimensional grids.

Bernard Haasdonk, Dietmar Kröner, Christian Rohde

Numerische Mathematik, 2001