Solving inverse problems in nonlinear PDEs by recurrent neural networks

Author

Uchiyama, Tadasu ; Sonehara, Noboru

Author_Institution

NTT Corp., Kanagawa, Japan

fYear

1993

fDate

1993

Firstpage

99

Abstract

A neural network approach for solving inverse problems in nonlinear partial differential equations (PDEs) is proposed, and a computer simulation based on this approach is described. The network is designed based on the differential difference equation (DDE) approximating the PDE. The network is trained so that its output and the known boundary values of connection weights and thresholds represent the approximated coefficients of the PDE governing the system. Simulation shows that the adjustable connections converge to the approximate values of the original coefficients identifying the system

Keywords

inverse problems; nonlinear differential equations; partial differential equations; recurrent neural nets; adjustable connections; approximate values; approximated coefficients; boundary values; connection weights; differential difference equation; inverse problems; nonlinear PDEs; partial differential equations; recurrent neural networks; Boundary conditions; Computer simulation; Difference equations; Differential equations; Intelligent networks; Inverse problems; Neural networks; Nonlinear equations; Partial differential equations; Recurrent neural networks;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1993., IEEE International Conference on

Conference_Location

San Francisco, CA

Print_ISBN

0-7803-0999-5

Type

conf

DOI

10.1109/ICNN.1993.298524

Filename

298524