Abstract: Radial basis function neural networks (RBFNN) which are best suited for nonlinear function approximation, have been successfully applied to a wide range of areas including system modeling.

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Article subjects are automatically applied from the ACS Subject Taxonomy and describe the scientific concepts and themes of the article. The application presented here utilizes the R Shiny platform to ...

ABSTRACT: Radial Basis Function methods for scattered data interpolation and for the numerical solution of PDEs were originally implemented in a global manner. Subsequently, it was realized that the ...

pwtools is a Python package for pre- and postprocessing of atomistic calculations, mostly targeted to Quantum Espresso, CPMD, CP2K and LAMMPS. It is almost, but not quite, entirely unlike ASE, with ...

ABSTRACT: Accurately approximating higher order derivatives is an inherently difficult problem. It is shown that a random variable shape parameter strategy can improve the accuracy of approximating ...

Abstract: This research paper proposes a novel approach for the linearization of non-linear thermocouple data using the Radial Basis Function (RBF) method. The proposed approach utilizes a different ...