Hybrid neural network/finite element modelling of wave propagation in infinite domains
The paper presents the method of formulation of a transmitting boundary using artificial neural network (ANN). The back-propagation neural networks (BPNN) [Comput. Struct. 79 (2001) 2261; S. Haykin, A Comprehensive Foundation, second ed., Prentice-Hall, 1999; Z. Waszczyszyn, in B.H.V. Topping (Ed.), Computational Mechanics for the Twenty-First Century, Civil-Comp Ltd., 2000, p. 479] are used for simulation of dynamic reactions on artificial boundary from an outside region. Neural networks are applied to all nodes on artificial boundary. The input vector for ANN is composed of (1) displacement, velocity and acceleration at time t?1 in node on the boundary, (2) displacements at time t?1 in three consecutive nodes on line perpendicular to the artificial boundary. The output vector determines the dynamic reactions at time t. Besides the displacements, velocities and accelerations at time t are computed. Three problems are discussed: (1) wave propagation in semi-infinite strip, (2) wave propagation in semi-infinite strip with notch, (3) acoustic radiation from nonconcentric radiator. Neural networks with one or two hidden layers were tested. Computations proved that BPNNs can be efficiently applied to the implementation of the approximate boundary condition on the artificial surface.
neural networks finite elements wave propagation infinite domain artificial boundary