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)

Online handwriting recognition with support vector machines - a kernel approach. - Claus Bahlmann; Bernard Haasdonk; Hans Burkhardt (IWFHR, 2002)

Tangent Distance Kernels for Support Vector Machines. - Bernard Haasdonk; Daniel Keysers (ICPR (2), 2002)

Multiresolution Visualization of Higher Order Adaptive Finite Element Simulations. - Bernard Haasdonk; Mario Ohlberger; Martin Rumpf; Alfred Schmidt; Kunibert G. Siebert (Computing, 2003)

Learning with Distance Substitution Kernels. - Bernard Haasdonk; Claus Bahlmann (DAGM-Symposium, 2004)

Adjustable Invariant Features by Partial Haar-Integration. - Bernard Haasdonk; Alaa Halawani; Hans Burkhardt (ICPR (2), 2004)

Invariance in Kernel Methods by Haar-Integration Kernels. - Bernard Haasdonk; A. Vossen; Hans Burkhardt (SCIA, 2005)

Feature Space Interpretation of SVMs with Indefinite Kernels. - Bernard Haasdonk (IEEE Trans. Pattern Anal. Mach. Intell., 2005)

Transformation knowledge in pattern analysis with kernel methods: distance and integration kernels. - Bernard Haasdonk (Shaker, 2006)

Classification with Invariant Distance Substitution Kernels. - Bernard Haasdonk; Hans Burkhardt (GfKl, 2007)

Invariant kernel functions for pattern analysis and machine learning. - Bernard Haasdonk; Hans Burkhardt (Mach. Learn., 2007)

Classification with Kernel Mahalanobis Distance Classifiers. - Bernard Haasdonk; Elzbieta Pekalska (GfKl, 2008)

Indefinite Kernel Fisher Discriminant. - Bernard Haasdonk; Elzbieta Pekalska (ICPR, 2008)

Reduced Basis Method for quadratically nonlinear transport equations. - Nadine Jung; Bernard Haasdonk; Dietmar Kröner (Int. J. Comput. Sci. Math., 2009)

Kernel Discriminant Analysis for Positive Definite and Indefinite Kernels. - Elzbieta Pekalska; Bernard Haasdonk (IEEE Trans. Pattern Anal. Mach. Intell., 2009)

Indefinite Kernel Discriminant Analysis. - Bernard Haasdonk; Elzbieta Pekalska (COMPSTAT, 2010)

Effiziente und gesicherte Modellreduktion für parametrisierte dynamische Systeme (Efficient and Certified Model Reduction for Parametrized Dynamical Systems). - Bernard Haasdonk (Autom., 2010)

Efficient parametric analysis of the chemical master equation through model order reduction. - Steffen Waldherr; Bernard Haasdonk (BMC Syst. Biol., 2012)

Efficient a-posteriori error estimation for nonlinear kernel-based reduced systems. - Daniel Wirtz; Bernard Haasdonk (Syst. Control. Lett., 2012)

A Reduced Basis Method for Parametrized Variational Inequalities. - Bernard Haasdonk; Julien Salomon; Barbara I. Wohlmuth (SIAM J. Numer. Anal., 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)

Output Error Bounds for the Dirichlet-Neumann Reduced Basis Method. - Immanuel Martini; Bernard Haasdonk (ENUMATH, 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)

A Posteriori Error Estimation for DEIM Reduced Nonlinear Dynamical Systems. - Daniel Wirtz; Danny C. Sorensen; Bernard Haasdonk (SIAM J. Sci. Comput., 2014)

Reduced basis approximation and a-posteriori error estimation for the coupled Stokes-Darcy system. - Immanuel Martini; Gianluigi Rozza; Bernard Haasdonk (Adv. Comput. Math., 2015)

A POD-EIM reduced two-scale model for crystal growth. - Magnus Redeker; Bernard Haasdonk (Adv. Comput. Math., 2015)

Certified PDE-constrained parameter optimization using reduced basis surrogate models for evolution problems. - Markus A. Dihlmann; Bernard Haasdonk (Comput. Optim. Appl., 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)

PEBL-ROM: Projection-error based local reduced-order models. - David Amsallem; Bernard Haasdonk (Adv. Model. Simul. Eng. Sci., 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)

Server-assisted interactive mobile simulations for pervasive applications. - Christoph Dibak; Andreas Schmidt 005; Frank Dürr; Bernard Haasdonk; Kurt Rothermel (PerCom, 2017)

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)

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)

Certified Reduced Basis Approximation for the Coupling of Viscous and Inviscid Parametrized Flow Models. - Immanuel Martini; Bernard Haasdonk; Gianluigi Rozza (J. Sci. Comput., 2018)

Enabling interactive mobile simulations through distributed reduced models. - Christoph Dibak; Bernard Haasdonk; Andreas Schmidt 005; Frank Dürr; Kurt Rothermel (Pervasive Mob. Comput., 2018)

Experimental Comparison of Symplectic and Non-symplectic Model Order Reduction on an Uncertainty Quantification Problem. - Patrick Buchfink; Bernard Haasdonk (ENUMATH, 2019)

Biomechanical Surrogate Modelling Using Stabilized Vectorial Greedy Kernel Methods. - Bernard Haasdonk; Tizian Wenzel; Gabriele Santin; Syn Schmitt (ENUMATH, 2019)

Convergence Rates for Matrix P-Greedy Variants. - Dominik Wittwar; Bernard Haasdonk (ENUMATH, 2019)

Kernel Methods for Surrogate Modeling. - 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)

A novel class of stabilized greedy kernel approximation algorithms: Convergence, stability & uniform point distribution. - Tizian Wenzel; Gabriele Santin; Bernard Haasdonk (CoRR, 2019)

Data-Driven Time Parallelism via Forecasting. - Kevin Carlberg; Lukas Brencher; Bernard Haasdonk; Andrea Barth (SIAM J. Sci. Comput., 2019)

Feedback control of parametrized PDEs via model order reduction and dynamic programming principle. - Alessandro Alla; Bernard Haasdonk; Andreas Schmidt 005 (Adv. Comput. Math., 2020)

Rigorous and effective a-posteriori error bounds for nonlinear problems - application to RB methods. - Andreas Schmidt 005; Dominik Wittwar; Bernard Haasdonk (Adv. Comput. Math., 2020)

Sampling based approximation of linear functionals in Reproducing Kernel Hilbert Spaces. - Gabriele Santin; Toni Karvonen; Bernard Haasdonk (CoRR, 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)

A Full Order, Reduced Order and Machine Learning Model Pipeline for Efficient Prediction of Reactive Flows. - Pavel Gavrilenko; Bernard Haasdonk; Oleg Iliev 001; Mario Ohlberger; Felix Schindler; Pavel Toktaliev; Tizian Wenzel; Maha Youssef (LSSC, 2021)

Reduced Basis Methods for Efficient Simulation of a Rigid Robot Hand Interacting with Soft Tissue. - Shahnewaz Shuva; Patrick Buchfink; Oliver Röhrle; Bernard Haasdonk (LSSC, 2021)

Structured Deep Kernel Networks for Data-Driven Closure Terms of Turbulent Flows. - Tizian Wenzel; Marius Kurz; Andrea Beck; Gabriele Santin; Bernard Haasdonk (LSSC, 2021)

Universality and Optimality of Structured Deep Kernel Networks. - Tizian Wenzel; Gabriele Santin; Bernard Haasdonk (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)

An adaptive model hierarchy for data-augmented training of kernel models for reactive flow. - Bernard Haasdonk; Mario Ohlberger; Felix Schindler (CoRR, 2021)

Symplectic Model Reduction of Hamiltonian Systems on Nonlinear Manifolds. - Patrick Buchfink; Silke Glas; Bernard Haasdonk (CoRR, 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)

Surrogate-data-enriched Physics-Aware Neural Networks. - Raphael Leiteritz; Patrick Buchfink; Bernard Haasdonk; Dirk Pflüger (NLDL, 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)

Stability of convergence rates: Kernel interpolation on non-Lipschitz domains. - Tizian Wenzel; Gabriele Santin; Bernard Haasdonk (CoRR, 2022)

Adaptive meshfree solution of linear partial differential equations with PDE-greedy kernel methods. - Tizian Wenzel; Daniel Winkle; Gabriele Santin; Bernard Haasdonk (CoRR, 2022)

Randomized Symplectic Model Order Reduction for Hamiltonian Systems. - Robin Herkert; Patrick Buchfink; Bernard Haasdonk; Johannes Rettberg; Jörg Fehr (LSSC, 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 (LSSC, 2023)

Approximation Bounds for Model Reduction on Polynomially Mapped Manifolds. - Patrick Buchfink; Silke Glas; Bernard Haasdonk (CoRR, 2023)

Model Reduction on Manifolds: A differential geometric framework. - Patrick Buchfink; Silke Glas; Bernard Haasdonk; Benjamin Unger (CoRR, 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., 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., 2023)

Error Analysis of Randomized Symplectic Model Order Reduction for Hamiltonian systems. - Robin Herkert; Patrick Buchfink; Bernard Haasdonk; Johannes Rettberg; Jörg Fehr (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 001 (CoRR, 2024)

Data-driven identification of latent port-Hamiltonian systems. - Johannes Rettberg; Jonas Kneifl; Julius Herb; Patrick Buchfink; Jörg Fehr; Bernard Haasdonk (CoRR, 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)

Dictionary-based online-adaptive structure-preserving model order reduction for parametric Hamiltonian systems. - Robin Herkert; Patrick Buchfink; Bernard Haasdonk (Adv. Comput. Math., 2024)

Hermite kernel surrogates for the value function of high-dimensional nonlinear optimal control problems. - Tobias Ehring; Bernard Haasdonk (Adv. Comput. Math., 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., 2024)

A trust-region framework for optimization using Hermite kernel surrogate models. - Sven Ullmann; Tobias Ehring; Robin Herkert; Bernard Haasdonk (CoRR, 2025)

Kernel-based Greedy Approximation of Parametric Elliptic Boundary Value Problems. - Bernard Haasdonk; Gabriele Santin; Tizian Wenzel (CoRR, 2025)

On the Convergence of the Policy Iteration for Infinite-Horizon Nonlinear Optimal Control Problems. - Tobias Ehring; Behzad Azmi; Bernard Haasdonk (CoRR, 2025)

Solving Approximation Tasks with Greedy Deep Kernel Methods. - Marian Klink; Tobias Ehring; Robin Herkert; Robin Lautenschlager; Dominik Göddeke; Bernard Haasdonk (CoRR, 2025)