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A Note on the Direct Approximation of Derivatives in Rational Radial Basis Functions Partition of Unity Method
Vahid Mohammadi, Stefano De Marchi
This paper proposes a Direct Rational Radial Basis Functions Partition of Unity (D-RRBF-PU) approach to compute derivatives of functions with steep gradients or discontinuities. Th…
On the Lebesgue constant of the Morrow-Patterson points
Tomasz Beberok, Leokadia Białas-Cież, Stefano De Marchi
The study of interpolation nodes and their associated Lebesgue constants are central to numerical analysis, impacting the stability and accuracy of polynomial approximations. In th…
Fast-Decaying Polynomial Reproduction
Stefano De Marchi, Giacomo Cappellazzo
Polynomial reproduction plays a relevant role in deriving error estimates for various approximation schemes. Local reproduction in a quasi-uniform setting is a significant factor i…
-convergence of Kantorovich-type Max-Min Neural Network Operators
İsmail Aslan, Stefano De Marchi, Wolfgang Erb
In this work, we study the Kantorovich variant of max-min neural network operators, in which the operator kernel is defined in terms of sigmoidal functions. Our main aim is to demo…
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…