An efficient indirect RBFN-based method for numerical solution of PDEs
Article
Article Title | An efficient indirect RBFN-based method for numerical solution of PDEs |
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ERA Journal ID | 344 |
Article Category | Article |
Authors | Mai-Duy, Nam (Author) and Tran-Cong, Thanh (Author) |
Journal Title | Numerical Methods for Partial Differential Equations |
Journal Citation | 21 (4), pp. 770-790 |
Number of Pages | 21 |
Year | 2005 |
Place of Publication | Hoboken, NJ. United States |
ISSN | 0749-159X |
1098-2426 | |
Digital Object Identifier (DOI) | https://doi.org/10.1002/num.20062 |
Web Address (URL) | http://www3.interscience.wiley.com/cgi-bin/fulltext/109864245/PDFSTART |
Abstract | This article presents an efficient indirect radial basis function network (RBFN) method for numerical solution of partial differential equations (PDEs). Previous findings showed that the RBFN method based on an integration process (IRBFN) is superior to the one based on a differentiation process (DRBFN) in terms of solution accuracy and convergence rate (Mai-Duy and Tran-Cong, Neural Networks 14(2) 2001, 185). However, when the problem dimensionality N is greater than 1, the size of the system of equations obtained in the former is about N times as big as that in the latter. In this article, prior conversions of the multiple spaces of network weights into the single space of function values are introduced in the IRBFN approach, thereby keeping the system matrix size small and comparable to that associated with the DRBFN approach. Furthermore, the nonlinear systems of equations obtained are solved with the use of trust region methods. The present approach yields very good results using relatively low numbers of data points. For example, in the simulation of driven cavity viscous flows, a high Reynolds number of 3200 is achieved using only 51 × 51 data points. |
Keywords | radial basis function; trust region methods; Poisson equation; Navier-Stokes equation; driven cavity viscous flow |
ANZSRC Field of Research 2020 | 401799. Mechanical engineering not elsewhere classified |
401213. Turbulent flows | |
490410. Partial differential equations | |
Public Notes | File reproduced in accordance with the copyright policy of the publisher/author. |
Byline Affiliations | University of Sydney |
Department of Mechanical and Mechatronic Engineering |
https://research.usq.edu.au/item/9y535/an-efficient-indirect-rbfn-based-method-for-numerical-solution-of-pdes
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