Bernard Haasdonk

发表

Charbel Farhat, David Amsallem, Bernard Haasdonk, 2015 .

Biomechanical surrogate modelling using stabilized vectorial greedy kernel methods pdf

Bernard Haasdonk, Syn Schmitt, Tizian Wenzel, 2020, ArXiv.

Tangent distance kernels for support vector machines

Bernard Haasdonk, Daniel Keysers, Daniel Keysers, 2002, Object recognition supported by user interaction for service robots.

Time-parallel reduced-order models via forecasting.

Bernard Haasdonk, Kevin Carlberg, Lukas Brencher, 2015 .

Numerical modelling of a peripheral arterial stenosis using dimensionally reduced models and kernel methods

Bernard Haasdonk, Rainer Helmig, Gabriele Santin, 2018, International journal for numerical methods in biomedical engineering.

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

Bernard Haasdonk, Markus A. Dihlmann, B. Haasdonk, 2015, Comput. Optim. Appl..

A Sensitivity Study of Error Estimation in Reduced Elastic Multibody Systems

Bernard Haasdonk, Dennis Grunert, Jörg Fehr, 2018 .

Server-assisted interactive mobile simulations for pervasive applications

Frank Dürr, Kurt Rothermel, Bernard Haasdonk, 2017, 2017 IEEE International Conference on Pervasive Computing and Communications (PerCom).

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

Gianluigi Rozza, Bernard Haasdonk, Immanuel Martini, 2015, Adv. Comput. Math..

A-posteriori error estimation for second order mechanical systems

Peter Eberhard, Bernard Haasdonk, Jörg Fehr, 2012 .

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

Bernard Haasdonk, Steffen Waldherr, 2012, BMC Systems Biology.

The localized reduced basis multiscale method for two‐phase flows in porous media

Knut-Andreas Lie, Bernard Haasdonk, Mario Ohlberger, 2014, 1405.2810.

Data-driven surrogates of value functions and applications to feedback control for dynamical systems

Bernard Haasdonk, André Schmidt, B. Haasdonk, 2018 .

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

Bernard Haasdonk, Daniel Wirtz, B. Haasdonk, 2012, Syst. Control. Lett..

Enabling Interactive Mobile Simulations Through Distributed Reduced Models pdf

Frank Dürr, Kurt Rothermel, Bernard Haasdonk, 2018, Pervasive Mob. Comput..

A Procedural Interface for Multiresolutional Visualization of General Numerical Data

Martin Rumpf, Ulrich Weikard, Mario Lenz, 1999 .

Effiziente und gesicherte Modellreduktion für parametrisierte dynamische Systeme

Bernard Haasdonk, 2010, Autom..

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

Bernard Haasdonk, Mario Ohlberger, Martin Drohmann, 2012, SIAM J. Sci. Comput..

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

Dirk Pflüger, Bernard Haasdonk, Andrea Barth, 2018, Computational Geosciences.

A Posteriori Error Estimation for DEIM Reduced Nonlinear Dynamical Systems

Danny C. Sorensen, Bernard Haasdonk, Daniel Wirtz, 2014, SIAM J. Sci. Comput..

Output Error Bounds for the Dirichlet-Neumann Reduced Basis Method

Bernard Haasdonk, Immanuel Martini, 2013, ENUMATH.

Sampling based approximation of linear functionals in reproducing kernel Hilbert spaces pdf

Bernard Haasdonk, Gabriele Santin, Toni Karvonen, 2020, BIT Numerical Mathematics.

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

Bernard Haasdonk, Tizian Wenzel, Gabriele Santin, 2021, J. Approx. Theory.

Multiresolution Visualization of Higher Order Adaptive Finite Element Simulations

Kunibert G. Siebert, Martin Rumpf, Alfred Schmidt, 2003, Computing.

Feature space interpretation of SVMs with indefinite kernels

Bernard Haasdonk, B. Haasdonk, 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Error Estimation for the Simulation of Elastic Multibody Systems

Bernard Haasdonk, Dennis Grunert, Jörg Fehr, 2018, PAMM.

Indefinite Kernel Discriminant Analysis

Bernard Haasdonk, Elżbieta Pȩkalska, E. Pekalska, 2010, COMPSTAT.

Learning with Distance Substitution Kernels

Claus Bahlmann, Bernard Haasdonk, B. Haasdonk, 2004, DAGM-Symposium.

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

David Amsallem, Bernard Haasdonk, D. Amsallem, 2016, Advanced Modeling and Simulation in Engineering Sciences.

Reduced Basis Method for quadratically nonlinear transport equations

Bernard Haasdonk, Dietmar Kröner, Nadine Jung, 2009, Int. J. Comput. Sci. Math..

Application of the Rigid Finite Element Method to the Simulation of Cable-Driven Parallel Robots

Andreas Pott, Bernard Haasdonk, Philipp Tempel, 2018 .

Chapter 2: Reduced Basis Methods for Parametrized PDEs—A Tutorial Introduction for Stationary and Instationary Problems

Bernard Haasdonk, B. Haasdonk, 2017 .

Indefinite Kernel Fisher Discriminant

Elzbieta Pekalska, Bernard Haasdonk, 2008, 2008 19th International Conference on Pattern Recognition.

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

Bernard Haasdonk, Alessandro Alla, Andreas Schmidt, 2018, Advances in Computational Mathematics.

Greedy Kernel Approximation for Sparse Surrogate Modeling

Bernard Haasdonk, Gabriele Santin, B. Haasdonk, 2018 .

Kernel Discriminant Analysis for Positive Definite and Indefinite Kernels

Elzbieta Pekalska, Bernard Haasdonk, E. Pekalska, 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Deep recurrent Gaussian process with variational Sparse Spectrum approximation pdf

Holger Ulmer, Bernard Haasdonk, Markus Hanselmann, 2017, ArXiv.

Invariance in Kernel Methods by Haar-Integration Kernels

Hans Burkhardt, Bernard Haasdonk, A. Vossen, 2005, SCIA.

Online handwriting recognition with support vector machines - a kernel approach

Claus Bahlmann, Hans Burkhardt, Bernard Haasdonk, 2002, Proceedings Eighth International Workshop on Frontiers in Handwriting Recognition.

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

Bernard Haasdonk, B. Haasdonk, 2006 .

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

Barbara I. Wohlmuth, Bernard Haasdonk, Julien Salomon, 2014, SIAM J. Financial Math..

A Software Framework for Reduced Basis Methods Using Dune-RB and RBmatlab

Bernard Haasdonk, Mario Ohlberger, Martin Drohmann, 2012 .

Data-driven time parallelism via forecasting pdf

Bernard Haasdonk, Andrea Barth, Kevin Carlberg, 2016, SIAM J. Sci. Comput..

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

Bernard Haasdonk, Christian Rohde, Dietmar Kröner, 2001, Numerische Mathematik.

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

Bernard Haasdonk, Magnus Redeker, B. Haasdonk, 2015, Adv. Comput. Math..

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

Karsten Urban, Bernard Haasdonk, Bernhard Wieland, 2013, SIAM/ASA J. Uncertain. Quantification.

Classification with Invariant Distance Substitution Kernels

Hans Burkhardt, Bernard Haasdonk, 2007, GfKl.

Data-driven time parallelism and model reduction.

Bernard Haasdonk, Kevin Carlberg, Lukas Brencher, 2016 .

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

Rainer Helmig, Bernard Haasdonk, Gabriele Santin, 2018, ArXiv.

A Reduced Basis Method for Parametrized Variational Inequalities

Barbara I. Wohlmuth, Bernard Haasdonk, Julien Salomon, 2012, SIAM J. Numer. Anal..

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

Gianluigi Rozza, Bernard Haasdonk, Immanuel Martini, 2018, J. Sci. Comput..

Adjustable invariant features by partial Haar-integration

Hans Burkhardt, Bernard Haasdonk, Alaa Halawani, 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

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

Bernard Haasdonk, Andreas Schmidt, Dominik Wittwar, 2020, Adv. Comput. Math..

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

Bernard Haasdonk, Gabriele Santin, Boumediene Hamzi, 2020, ArXiv.

Invariant kernel functions for pattern analysis and machine learning

Hans Burkhardt, Bernard Haasdonk, H. Burkhardt, 2007, Machine Learning.

A full order, reduced order and machine learning model pipeline for efficient prediction of reactive flows pdf

Pavel Toktaliev, Bernard Haasdonk, Mario Ohlberger, 2021, ArXiv.

Surrogate-data-enriched Physics-Aware Neural Networks pdf

Dirk Pflüger, Bernard Haasdonk, Raphael Leiteritz, 2021, NLDL.

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

Bernard Haasdonk, Tizian Wenzel, Gabriele Santin, 2021, J. Approx. Theory.

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

Bernard Haasdonk, Oliver Röhrle, Shahnewaz Shuva, 2021, ArXiv.

Symplectic Model Reduction of Hamiltonian Systems on Nonlinear Manifolds pdf

Bernard Haasdonk, Silke Glas, Patrick Buchfink, 2021, ArXiv.

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

Bernard Haasdonk, Gabriele Santin, Toni Karvonen, 2021, BIT Numerical Mathematics.

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

Bernard Haasdonk, Tizian Wenzel, Gabriele Santin, 2021, ArXiv.

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

Bernard Haasdonk, Tizian Wenzel, Gabriele Santin, 2021, ArXiv.

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

Bernard Haasdonk, Mario Ohlberger, Felix Schindler, 2021, ArXiv.

Universality and Optimality of Structured Deep Kernel Networks pdf

Bernard Haasdonk, Tizian Wenzel, Gabriele Santin, 2021, ArXiv.

Model order reduction and error estimation with an application to the parameter-dependent eddy current equation

Boris Lohmann, Anthony T. Patera, Bernard Haasdonk, 2011 .

Model reduction of parametrized evolution problems using the reduced basis method with adaptive time partitioning

Bernard Haasdonk, Martin Drohmann, Markus Dihlmann, 2011 .

Feature Space Interpretation of SVMs with non Positive Definite Kernels Internal Report 1 / 03

Bernard Haasdonk, B. Haasdonk, 2003 .

A Reduced Basis Method for Evolution Schemes with Parameter-Dependent Explicit Operators

Gianluigi Rozza, Bernard Haasdonk, Mario Ohlberger, 2008 .

Adaptive Basis Enrichment for the Reduced Basis Method Applied to Finite Volume Schemes

Bernard Haasdonk, Mario Ohlberger, B. Haasdonk, 2008 .

Certified Nonlinear Parameter Optimization with Reduced Basis Surrogate Models

Bernard Haasdonk, Markus Dihlmann, B. Haasdonk, 2013 .

A-Posteriori Error Estimation for Parameterized Kernel-Based Systems

Bernard Haasdonk, Daniel Wirtz, B. Haasdonk, 2012 .

A training set and multiple bases generation approach for parameterized model reduction based on adaptive grids in parameter space

Bernard Haasdonk, Mario Ohlberger, Markus Dihlmann, 2011 .

THE LOCALIZED REDUCED BASIS MULTISCALE METHOD

Bernard Haasdonk, Mario Ohlberger, Sven Kaulmann, 2015 .

Reduced Basis Method for Finite Volume Approximations of Parametrized Evolution Equations

Bernard Haasdonk, Mario Ohlberger, B. Haasdonk, 2006 .

A Reduced Basis Method for the Simulation of American Options

Bernard Haasdonk, Julien Salomon, Barbara Wohlmuth, 2012, 1201.3289.

SPACE-ADAPTIVE REDUCED BASIS SIMULATION FOR TIME-DEPENDENT PROBLEMS

Bernard Haasdonk, Mario Ohlberger, 2008 .

Greedy‐based approximation of frequency‐weighted Gramian matrices for model reduction in multibody dynamics

Peter Eberhard, Bernard Haasdonk, Jörg Fehr, 2013 .

Reduced Basis Model Reduction of Parametrized Two—Phase Flow in Porous Media

Bernard Haasdonk, Mario Ohlberger, Martin Drohmann, 2012 .

Surrogate modeling of multiscale models using kernel methods

Bernard Haasdonk, Daniel Wirtz, N. Karajan, 2015 .

Reduced basis approximation of large scale parametric algebraic Riccati equations

Bernard Haasdonk, B. Haasdonk, A. Schmidt, 2018 .

Reduced Basis Method for Explicit Finite Volume Approximations of Nonlinear Conservation Laws

Bernard Haasdonk, Mario Ohlberger, 2008 .

Convergence rate of the data-independent P-greedy algorithm in kernel-based approximation

Bernard Haasdonk, Gabriele Santin, B. Haasdonk, 2016, 1612.02672.

A Dirichlet-Neumann reduced basis method for homogeneous domain decomposition problems

Bernard Haasdonk, I. Maier, 2014 .

Model Order Reduction Approaches for Infinite Horizon Optimal Control Problems via the HJB Equation

Bernard Haasdonk, Alessandro Alla, B. Haasdonk, 2016, 1607.02337.

A Vectorial Kernel Orthogonal Greedy Algorithm

Bernard Haasdonk, Daniel Wirtz, B. Haasdonk, 2013 .

Adaptive Reduced Basis Methods for Nonlinear Convection–Diffusion Equations

Bernard Haasdonk, Mario Ohlberger, Martin Drohmann, 2011 .

Greedy Kernel Methods for Center Manifold Approximation

Bernard Haasdonk, Gabriele Santin, Boumediene Hamzi, 2018, 1810.11329.

Online Reduced Basis Construction Procedure for Model Reduction of Parametrized Evolution Systems

Bernard Haasdonk, Markus Dihlmann, Sven Kaulmann, 2012 .

Comparison of Methods for Parametric Model Order Reduction of Time-Dependent Problems

Peter Benner, Bernard Haasdonk, Ulrike Baur, 2017 .

Convergence Rates of the POD–Greedy Method

Bernard Haasdonk, B. Haasdonk, 2013 .