By Topic

Solving partial differential equations in real-time using artificial neural network signal processing as an alternative to finite-element analysis

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Mingui Sun ; Dept. of Neurosurg., Pittsburgh Univ., PA, USA ; Xiaopu Yan ; Sclabassi, R.J.

Finite element methods (FEM) have been widely utilized for evaluating partial differential equations (PDEs). Although these methods have been highly successful, they require time-consuming procedures to build numerous volumetric elements and solve large-size linear systems of equations. In this paper, a new signal processing method is utilized to solve PDEs numerically by using an artificial neural network. We investigate the theoretical aspects of this approach and show that the numerical computation can be formulated as a machining learning problem and implemented by a supervised function approximation neural network. We also show that, for the case of the Poisson equation, the solution is unique and continuous with respect to the boundary surface. We apply this method to bio-potential computation where the solution of a standard volume conductor is mapped to the solutions of a set of volume conductors in different shapes.

Published in:

Neural Networks and Signal Processing, 2003. Proceedings of the 2003 International Conference on  (Volume:1 )

Date of Conference:

14-17 Dec. 2003