
An adaptive model hierarchy for dataaugmented training of kernel models for reactive flow
We consider machinelearning of timedependent quantities of interest de...
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Analysis of target datadependent greedy kernel algorithms: Convergence rates for f, f · P and f/Pgreedy
Datadependent greedy algorithms in kernel spaces are known to provide f...
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Universality and Optimality of Structured Deep Kernel Networks
Kernel based methods yield approximation models that are flexible, effic...
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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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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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Structured Deep Kernel Networks for DataDriven Closure Terms of Turbulent Flows
Standard kernel methods for machine learning usually struggle when deali...
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Kernel methods for center manifold approximation and a databased version of the Center Manifold Theorem
For dynamical systems with a non hyperbolic equilibrium, it is possible ...
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Biomechanical surrogate modelling using stabilized vectorial greedy kernel methods
Greedy kernel approximation algorithms are successful techniques for spa...
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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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A novel class of stabilized greedy kernel approximation algorithms: Convergence, stability uniform point distribution
Kernel based methods provide a way to reconstruct potentially highdimen...
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Kernel Methods for Surrogate Modeling
This chapter deals with kernel methods as a special class of techniques ...
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Enabling Interactive Mobile Simulations Through Distributed Reduced Models
Currently, various hardware and software companies are developing augmen...
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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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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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Comparison of datadriven 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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Deep Recurrent Gaussian Process with Variational Sparse Spectrum Approximation
Modeling sequential data has become more and more important in practice....
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Bernard Haasdonk
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