Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
9653617 | Neurocomputing | 2005 | 17 Pages |
Abstract
Basic learning algorithms and the neural network model are applied to the problem of mesh adaptation for the finite-element method for solving time-dependent partial differential equations. Time series prediction via the neural network methodology is used to predict the areas of “interest” in order to obtain an effective mesh refinement at the appropriate times. This allows for increased numerical accuracy with the same computational resources as compared with more “traditional” methods.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Larry Manevitz, Akram Bitar, Dan Givoli,