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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.>
Uchiyama et al. (Mon,) studied this question.