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Neural network error correction for solving coupled ordinary differential equations

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4 Author(s)
R. O. Shelton ; Lyndon B. Johnson Space Center, Houston, TX, USA ; J. A. Darsey ; B. G. Sumpter ; D. W. Noid

A neural network is presented to learn errors generated by a numerical algorithm for solving coupled nonlinear differential equations. The method is based on using a neural network to correctly learn the error generated by, for example, Runge-Kutta on a model molecular dynamics (MD) problem. The neural network programs used in this study were developed by NASA. Comparisons are made for training the neural network using backpropagation and a new method (FLUB, fast learning utility for backpropagation) which was found to converge with fewer iterations. The neural net programs, the MD model and the calculations are discussed

Published in:

Neural Networks, 1992. IJCNN., International Joint Conference on  (Volume:4 )

Date of Conference:

7-11 Jun 1992