Kernel based methods provide a way to reconstruct potentially high-dimensional functions
from meshfree samples, ie, sampling points and corresponding target values. A crucial …

Data-dependent greedy algorithms in kernel spaces are known to provide fast converging
interpolants, while being extremely easy to implement and efficient to run. Despite this …

This chapter deals with kernel methods as a special class of techniques for surrogate
modeling. Kernel methods have proven to be efficient in machine learning, pattern …

A variety of methods is available to quantify uncertainties arising within the modeling of flow
and transport in carbon dioxide storage, but there is a lack of thorough comparisons …

In this work, we consider 2 kinds of model reduction techniques to simulate blood flow
through the largest systemic arteries, where a stenosis is located in a peripheral artery, ie, in …

In this paper we consider the problem of approximating vector-valued functions over a
domain $\Omega $. For this purpose, we use matrix-valued reproducing kernels, which can …

We illustrate and test an approach grounded on embedding moment equations (MEs) of
groundwater flow within a Monte Carlo based modeling strategy to yield a Reduced-Order …

In the current work, an efficient and powerful computational technique is implemented to
simulate an anomalous mobile-immobile solute transport process. The process is …

Kernel machines are widely used tools for extracting features from given data. In this context,
there are many available techniques that are able to predict, within a certain tolerance, the …

(Rephrased) Non-conformance decision-making processes in high-precision manufacturing
of engineering structures are often delayed due to numerical simulations that are needed for …