Please use this identifier to cite or link to this item: http://hdl.handle.net/11400/6226
Title: Neural network based derivation of efficient high order runge-kutta-nystrom pairs for the integration of orbits
Authors: Φαμέλης, Ιωάννης Θ.
Item type: Journal article
Keywords: Kepler problem;Neural networks;Πρόβλημα κέπλερ;Νευρωνικό δίκτυο
Subjects: Physics
Mathematical physics
Φυσική
Μαθηματική φυσική
Issue Date: 14-Feb-2015
2011
Publisher: World Scienti c Publishing Company
Abstract: We use Neural Network approach to derive a Runge{Kutta{Nystr om pair of orders 8(6) for the integration of orbital problems. We use an di erential evolution optimization technique to choose the free parameters of the method's family. We train the method to perform optimally in a speci c test orbit from the Kepler problem for a speci c tolerance. Our measure of e ciency involves the global error and the number of function evaluations. Other orbital problems are solved to test the new pair.
Language: English
Citation: Famelis, I. 9December 2011). Neural network based derivation of efficient high order runge-kutta-nystrom pairs for the integration of orbits. International Journal of Modern Physics C. 22(12). pp. 469–473. World Scientific Publishing Company: 2011.
Journal: International Journal of Modern Physics C
Type of Journal: With a review process (peer review)
Access scheme: Embargo
License: Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ηνωμένες Πολιτείες
URI: http://hdl.handle.net/11400/6226
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