Bernard Haasdonk

According to our database1, Bernard Haasdonk authored at least 68 papers between 2001 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
Dictionary-based online-adaptive structure-preserving model order reduction for parametric Hamiltonian systems.
Adv. Comput. Math., February, 2024

2023
A New Certified Hierarchical and Adaptive RB-ML-ROM Surrogate Model for Parametrized PDEs.
SIAM J. Sci. Comput., June, 2023

Symplectic Model Reduction of Hamiltonian Systems on Nonlinear Manifolds and Approximation with Weakly Symplectic Autoencoder.
SIAM J. Sci. Comput., April, 2023

Model Reduction on Manifolds: A differential geometric framework.
CoRR, 2023

Approximation Bounds for Model Reduction on Polynomially Mapped Manifolds.
CoRR, 2023

On the optimality of target-data-dependent kernel greedy interpolation in Sobolev Reproducing Kernel Hilbert Spaces.
CoRR, 2023

Hermite kernel surrogates for the value function of high-dimensional nonlinear optimal control problems.
CoRR, 2023

Improved a posteriori Error Bounds for Reduced port-Hamiltonian Systems.
CoRR, 2023

Randomized Symplectic Model Order Reduction for Hamiltonian Systems.
CoRR, 2023

Application of Deep Kernel Models for Certified and Adaptive RB-ML-ROM Surrogate Modeling.
CoRR, 2023

2022
Adaptive meshfree solution of linear partial differential equations with PDE-greedy kernel methods.
CoRR, 2022

Stability of convergence rates: Kernel interpolation on non-Lipschitz domains.
CoRR, 2022

Port-Hamiltonian Fluid-Structure Interaction Modeling and Structure-Preserving Model Order Reduction of a Classical Guitar.
CoRR, 2022

Surrogate-data-enriched Physics-Aware Neural Networks.
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.
J. Approx. Theory, 2021

Symplectic Model Reduction of Hamiltonian Systems on Nonlinear Manifolds.
CoRR, 2021

An adaptive model hierarchy for data-augmented training of kernel models for reactive flow.
CoRR, 2021

Analysis of target data-dependent greedy kernel algorithms: Convergence rates for f-, $f \cdot P$- and f/P-greedy.
CoRR, 2021

Universality and Optimality of Structured Deep Kernel Networks.
CoRR, 2021

Structured Deep Kernel Networks for Data-Driven Closure Terms of Turbulent Flows.
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.
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.
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.
CoRR, 2020

Sampling based approximation of linear functionals in Reproducing Kernel Hilbert Spaces.
CoRR, 2020

Rigorous and effective a-posteriori error bounds for nonlinear problems - application to RB methods.
Adv. Comput. Math., 2020

Feedback control of parametrized PDEs via model order reduction and dynamic programming principle.
Adv. Comput. Math., 2020

2019
Data-Driven Time Parallelism via Forecasting.
SIAM J. Sci. Comput., 2019

A novel class of stabilized greedy kernel approximation algorithms: Convergence, stability & uniform point distribution.
CoRR, 2019

Deep recurrent Gaussian process with variational Sparse Spectrum approximation.
CoRR, 2019

Kernel Methods for Surrogate Modeling.
CoRR, 2019

Convergence Rates for Matrix P-Greedy Variants.
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.
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.
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.
Pervasive Mob. Comput., 2018

Certified Reduced Basis Approximation for the Coupling of Viscous and Inviscid Parametrized Flow Models.
J. Sci. Comput., 2018

Numerical modelling of a peripheral arterial stenosis using dimensionally reduced models and machine learning techniques.
CoRR, 2018

Comparison of data-driven uncertainty quantification methods for a carbon dioxide storage benchmark scenario.
CoRR, 2018

2017
Server-assisted interactive mobile simulations for pervasive applications.
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.
CoRR, 2016

PEBL-ROM: Projection-error based local reduced-order models.
Adv. Model. Simul. Eng. Sci., 2016

2015
Reduced Basis Methods for Pricing Options with the Black-Scholes and Heston Models.
SIAM J. Financial Math., 2015

Certified PDE-constrained parameter optimization using reduced basis surrogate models for evolution problems.
Comput. Optim. Appl., 2015

A POD-EIM reduced two-scale model for crystal growth.
Adv. Comput. Math., 2015

Reduced basis approximation and a-posteriori error estimation for the coupled Stokes-Darcy system.
Adv. Comput. Math., 2015

2014
A Posteriori Error Estimation for DEIM Reduced Nonlinear Dynamical Systems.
SIAM J. Sci. Comput., 2014

2013
Reduced Basis Methods for Parameterized Partial Differential Equations with Stochastic Influences Using the Karhunen-Loève Expansion.
SIAM/ASA J. Uncertain. Quantification, 2013

Output Error Bounds for the Dirichlet-Neumann Reduced Basis Method.
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.
SIAM J. Sci. Comput., 2012

A Reduced Basis Method for Parametrized Variational Inequalities.
SIAM J. Numer. Anal., 2012

Efficient a-posteriori error estimation for nonlinear kernel-based reduced systems.
Syst. Control. Lett., 2012

Efficient parametric analysis of the chemical master equation through model order reduction.
BMC Syst. Biol., 2012

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

Indefinite Kernel Discriminant Analysis.
Proceedings of the 19th International Conference on Computational Statistics, 2010

2009
Kernel Discriminant Analysis for Positive Definite and Indefinite Kernels.
IEEE Trans. Pattern Anal. Mach. Intell., 2009

Reduced Basis Method for quadratically nonlinear transport equations.
Int. J. Comput. Sci. Math., 2009

2008
Indefinite Kernel Fisher Discriminant.
Proceedings of the 19th International Conference on Pattern Recognition (ICPR 2008), 2008

Classification with Kernel Mahalanobis Distance Classifiers.
Proceedings of the Advances in Data Analysis, Data Handling and Business Intelligence, 2008

2007
Invariant kernel functions for pattern analysis and machine learning.
Mach. Learn., 2007

Classification with Invariant Distance Substitution Kernels.
Proceedings of the Data Analysis, Machine Learning and Applications, 2007

2006
Transformation knowledge in pattern analysis with kernel methods: distance and integration kernels.
PhD thesis, 2006

2005
Feature Space Interpretation of SVMs with Indefinite Kernels.
IEEE Trans. Pattern Anal. Mach. Intell., 2005

Invariance in Kernel Methods by Haar-Integration Kernels.
Proceedings of the Image Analysis, 14th Scandinavian Conference, 2005

2004
Adjustable Invariant Features by Partial Haar-Integration.
Proceedings of the 17th International Conference on Pattern Recognition, 2004

Learning with Distance Substitution Kernels.
Proceedings of the Pattern Recognition, 26th DAGM Symposium, August 30, 2004

2003
Multiresolution Visualization of Higher Order Adaptive Finite Element Simulations.
Computing, 2003

2002
Tangent Distance Kernels for Support Vector Machines.
Proceedings of the 16th International Conference on Pattern Recognition, 2002

Online handwriting recognition with support vector machines - a kernel approach.
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.
Numerische Mathematik, 2001


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