most citedPhysics-driven machine learning for the prediction of coronal mass ejections' travel times

1 citations · 1 across the 5 of their papers we have counts for

collaborators
Showing math.NAShow all

4 papers · 1 filter

math.NA2023

A fast surrogate cross validation algorithm for meshfree RBF collocation approaches

Francesco Marchetti

Cross validation is an important tool in the RBF collocation setting, especially for the crucial tuning of the shape parameter related to the radial basis function. In this paper,…

math.NA2023

Mapped Variably Scaled Kernels: Applications to Solar Imaging

Francesco Marchetti, Emma Perracchione, Anna Volpara +3

Variably scaled kernels and mapped bases constructed via the so-called fake nodes approach are two different strategies to provide adaptive bases for function interpolation. In thi…

math.NA2023

Moving Least Squares Approximation using Variably Scaled Discontinuous Weight Function

Mohammad Karimnejad Esfahani, Stefano De Marchi, Francesco Marchetti

Functions with discontinuities appear in many applications such as image reconstruction, signal processing, optimal control problems, interface problems, engineering applications a…

math.NA2023

Data-driven kernel designs for optimized greedy schemes: A machine learning perspective

Tizian Wenzel, Francesco Marchetti, Emma Perracchione

Thanks to their easy implementation via Radial Basis Functions (RBFs), meshfree kernel methods have been proved to be an effective tool for e.g. scattered data interpolation, PDE c…