Neural Network Methods for Boundary Value Problems Defined in Arbitrarily Shaped Domains

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dc.creatorLagaris, I. E.
dc.creatorLikas, A.
dc.creatorPapageorgiou, D. G.
dc.date1998-12-03
dc.date.accessioned2026-07-07T03:23:51Z
dc.date.available2026-07-07T03:23:51Z
dc.descriptionPartial differential equations (PDEs) with Dirichlet boundary conditions defined on boundaries with simple geometry have been succesfuly treated using sigmoidal multilayer perceptrons in previous works. This article deals with the case of complex boundary geometry, where the boundary is determined by a number of points that belong to it and are closely located, so as to offer a reasonable representation. Two networks are employed: a multilayer perceptron and a radial basis function network. The later is used to account for the satisfaction of the boundary conditions. The method has been successfuly tested on two-dimensional and three-dimensional PDEs and has yielded accurate solutions.
dc.identifierhttps://arxiv.org/abs/cs/9812003
dc.identifierhttp://arxiv.org/abs/cs/9812003
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/33104
dc.subjectNeural and Evolutionary Computing
dc.subjectDisordered Systems and Neural Networks
dc.subjectNumerical Analysis
dc.subjectMathematical Physics
dc.subjectComputational Physics
dc.subjectC.1.3
dc.titleNeural Network Methods for Boundary Value Problems Defined in Arbitrarily Shaped Domains
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