Approximation of functions and their derivatives: A neural network implementation with applications
type: Item
instance of: scholarly article
title: Approximation of functions and their derivatives: A neural network implementation with applications
zbMATH Open document ID: 0939.65027
DOI: 10.1016/S0307-904X(99)00006-2
author: Yanyan Li
published in: Applied Mathematical Modelling
publication date: 2000-01-17T00:00:00Z
review text: The authors apply neural network techniques for the numerical approximation of functions of several variables and their first- and second-order partial derivatives. In order to eliminate the need of domain discretization, they propose to use a neural network based approximation of functions of several variables and their first- and second-order partial derivatives combined with a nonlinear boundary element method. Numerical results for functions of one, two and three variables are presented. Examples of viscoelastic fluid flows of a polymeric liquid (planar Couette and Poiseuille flows) are included.
Mathematics Subject Classification ID: 65D25
Mathematics Subject Classification ID: 74D10
Mathematics Subject Classification ID: 74S15
Mathematics Subject Classification ID: 82D60
Mathematics Subject Classification ID: 82B80
zbMATH DE Number: 1389069
zbMATH Keywords: neural network
zbMATH Keywords: function derivatives approximation
zbMATH Keywords: boundary element method
zbMATH Keywords: planar Couette and Poiseuille flows
zbMATH Keywords: numerical results
zbMATH Keywords: partial derivatives
zbMATH Keywords: viscoelastic fluid flows
zbMATH Keywords: polymeric liquid
describes a project that uses: PVM
MaRDI profile type: Publication
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description: scientific article; zbMATH DE number 1389069