Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
500725 | Computer Methods in Applied Mechanics and Engineering | 2005 | 21 Pages |
In this paper, an enhanced hybrid method (EHM) is presented for the simulation of homogeneous non-Gaussian stochastic fields with prescribed target marginal distribution and spectral density function. The presented methodology constitutes an efficient blending of the Deodatis–Micaletti method with a neural network based function approximation. Precisely, the function fitting ability of neural networks based on the resilient back-propagation (Rprop) learning algorithm is employed to approximate the unknown underlying Gaussian spectrum. The resulting algorithm can be successfully applied for simulating narrow-banded fields with very large skewness at a fraction of the computing time required by the existing methods. Its computational efficiency is demonstrated in three numerical examples involving fields that follow the beta and lognormal distributions.