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dc.contributor.authorKhalil, Omar-
dc.contributor.authorEl-Sharkawy, Hany-
dc.contributor.authorYoussef, Maha-
dc.contributor.authorBaumann, Gerd-
dc.date.accessioned2024-03-25T15:10:58Z-
dc.date.available2024-03-25T15:10:58Z-
dc.date.issued2022de
dc.identifier.issn1999-4893-
dc.identifier.other1884306640-
dc.identifier.urihttp://nbn-resolving.de/urn:nbn:de:bsz:93-opus-ds-141367de
dc.identifier.urihttp://elib.uni-stuttgart.de/handle/11682/14136-
dc.identifier.urihttp://dx.doi.org/10.18419/opus-14117-
dc.description.abstractWe propose a new method of adaptive piecewise approximation based on Sinc points for ordinary differential equations. The adaptive method is a piecewise collocation method which utilizes Poly-Sinc interpolation to reach a preset level of accuracy for the approximation. Our work extends the adaptive piecewise Poly-Sinc method to function approximation, for which we derived an a priori error estimate for our adaptive method and showed its exponential convergence in the number of iterations. In this work, we show the exponential convergence in the number of iterations of the a priori error estimate obtained from the piecewise collocation method, provided that a good estimate of the exact solution of the ordinary differential equation at the Sinc points exists. We use a statistical approach for partition refinement. The adaptive greedy piecewise Poly-Sinc algorithm is validated on regular and stiff ordinary differential equations.en
dc.description.sponsorshipBMBFde
dc.language.isoende
dc.relation.uridoi:10.3390/a15090320de
dc.rightsinfo:eu-repo/semantics/openAccessde
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/de
dc.subject.ddc510de
dc.titleAdaptive piecewise Poly-Sinc methods for ordinary differential equationsen
dc.typearticlede
dc.date.updated2023-11-14T00:12:15Z-
ubs.fakultaetMathematik und Physikde
ubs.fakultaetFakultätsübergreifend / Sonstige Einrichtungde
ubs.institutInstitut für Angewandte Analysis und numerische Simulationde
ubs.institutFakultätsübergreifend / Sonstige Einrichtungde
ubs.publikation.seiten27de
ubs.publikation.sourceAlgorithms 15 (2022), No. 320de
ubs.publikation.typZeitschriftenartikelde
Enthalten in den Sammlungen:08 Fakultät Mathematik und Physik

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