Kernel interpolation is a versatile tool for the approximation of functi...
Numerical methods for the optimal feedback control of high-dimensional
d...
Classical model order reduction (MOR) for parametric problems may become...
Projection-based model order reduction of dynamical systems usually
intr...
Simulations of large scale dynamical systems in multi-query or real-time...
In the framework of reduced basis methods, we recently introduced a new
...
We consider the meshless solution of PDEs via symmetric kernel collocati...
Error estimates for kernel interpolation in Reproducing Kernel Hilbert S...
A fluid-structure interaction model in a port-Hamiltonian representation...
Classical model reduction techniques project the governing equations ont...
Neural networks can be used as surrogates for PDE models. They can be ma...
We consider machine-learning of time-dependent quantities of interest de...
Data-dependent greedy algorithms in kernel spaces are known to provide f...
Kernel based methods yield approximation models that are flexible, effic...
We present an integrated approach for the use of simulated data from ful...
We present efficient reduced basis (RB) methods for the simulation of th...
Standard kernel methods for machine learning usually struggle when deali...
For dynamical systems with a non hyperbolic equilibrium, it is possible ...
Greedy kernel approximation algorithms are successful techniques for spa...
In this paper we analyze a greedy procedure to approximate a linear
func...
Kernel based methods provide a way to reconstruct potentially
high-dimen...
This chapter deals with kernel methods as a special class of techniques ...
Currently, various hardware and software companies are developing augmen...
In this work, we consider two kinds of model reduction techniques to sim...
In this work, we consider two kinds of model reduction techniques to sim...
A variety of methods is available to quantify uncertainties arising with...
Modeling sequential data has become more and more important in practice....