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07/19/2023
On the optimality of target-data-dependent kernel greedy interpolation in Sobolev Reproducing Kernel Hilbert Spaces
Kernel interpolation is a versatile tool for the approximation of functi...
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05/10/2023
Hermite kernel surrogates for the value function of high-dimensional nonlinear optimal control problems
Numerical methods for the optimal feedback control of high-dimensional d...
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03/31/2023
Dictionary-based Online-adaptive Structure-preserving Model Order Reduction for Parametric Hamiltonian Systems
Classical model order reduction (MOR) for parametric problems may become...
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03/30/2023
Improved a posteriori Error Bounds for Reduced port-Hamiltonian Systems
Projection-based model order reduction of dynamical systems usually intr...
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03/07/2023
Randomized Symplectic Model Order Reduction for Hamiltonian Systems
Simulations of large scale dynamical systems in multi-query or real-time...
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02/28/2023
Application of Deep Kernel Models for Certified and Adaptive RB-ML-ROM Surrogate Modeling
In the framework of reduced basis methods, we recently introduced a new ...
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07/28/2022
Adaptive meshfree solution of linear partial differential equations with PDE-greedy kernel methods
We consider the meshless solution of PDEs via symmetric kernel collocati...
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03/23/2022
Stability of convergence rates: Kernel interpolation on non-Lipschitz domains
Error estimates for kernel interpolation in Reproducing Kernel Hilbert S...
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03/18/2022
Port-Hamiltonian Fluid-Structure Interaction Modeling and Structure-Preserving Model Order Reduction of a Classical Guitar
A fluid-structure interaction model in a port-Hamiltonian representation...
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12/20/2021
Symplectic Model Reduction of Hamiltonian Systems on Nonlinear Manifolds
Classical model reduction techniques project the governing equations ont...
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12/10/2021
Surrogate-data-enriched Physics-Aware Neural Networks
Neural networks can be used as surrogates for PDE models. They can be ma...
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10/24/2021
An adaptive model hierarchy for data-augmented training of kernel models for reactive flow
We consider machine-learning of time-dependent quantities of interest de...
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05/16/2021
Analysis of target data-dependent greedy kernel algorithms: Convergence rates for f-, f · P- and f/P-greedy
Data-dependent greedy algorithms in kernel spaces are known to provide f...
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05/15/2021
Universality and Optimality of Structured Deep Kernel Networks
Kernel based methods yield approximation models that are flexible, effic...
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04/06/2021
A full order, reduced order and machine learning model pipeline for efficient prediction of reactive flows
We present an integrated approach for the use of simulated data from ful...
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03/29/2021
Reduced Basis Methods for Efficient Simulation of a Rigid Robot Hand Interacting with Soft Tissue
We present efficient reduced basis (RB) methods for the simulation of th...
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03/25/2021
Structured Deep Kernel Networks for Data-Driven Closure Terms of Turbulent Flows
Standard kernel methods for machine learning usually struggle when deali...
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12/01/2020
Kernel methods for center manifold approximation and a data-based version of the Center Manifold Theorem
For dynamical systems with a non hyperbolic equilibrium, it is possible ...
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04/27/2020
Biomechanical surrogate modelling using stabilized vectorial greedy kernel methods
Greedy kernel approximation algorithms are successful techniques for spa...
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04/01/2020
Sampling based approximation of linear functionals in Reproducing Kernel Hilbert Spaces
In this paper we analyze a greedy procedure to approximate a linear func...
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11/11/2019
A novel class of stabilized greedy kernel approximation algorithms: Convergence, stability uniform point distribution
Kernel based methods provide a way to reconstruct potentially high-dimen...
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07/24/2019
Kernel Methods for Surrogate Modeling
This chapter deals with kernel methods as a special class of techniques ...
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02/14/2018
Enabling Interactive Mobile Simulations Through Distributed Reduced Models
Currently, various hardware and software companies are developing augmen...
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02/13/2018
Numerical modelling of a peripheral arterial stenosis using dimensionally reduced models and kernel methods
In this work, we consider two kinds of model reduction techniques to sim...
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02/13/2018
Numerical modelling of a peripheral arterial stenosis using dimensionally reduced models and machine learning techniques
In this work, we consider two kinds of model reduction techniques to sim...
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02/08/2018
Comparison of data-driven uncertainty quantification methods for a carbon dioxide storage benchmark scenario
A variety of methods is available to quantify uncertainties arising with...
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11/02/2017