Neural networks in computational mechanics

• Autores: G. Yagawa, H. Okuda
• Localización: Archives of computational methods in engineering: state of the art reviews, ISSN 1134-3060, Vol. 3, Nº. 4, 1996, págs. 435-512
• Idioma: inglés
• DOI: 10.1007/bf02818935
• Texto completo no disponible (Saber más ...)
• Resumen

  • In this paper, recent neural network applications, epecially to the fields related with the computational mechanics, were surveyed. The most outstanding characteristics of the neural network aided computation is that neither complicated programmings nor rigid algorithms are needed. Another important point is that the neural network's inherent parallelism, that is, concurrent signal transmissions over numerous, information processing elements suits the massively parallel computer architectures. First, we briefly review the neural network applications to the computational mechanics fields from recent publications, and describe the mathematical basis of the neural network. Next, the following topics are detailed: quantitative nondestructive evaluation, structural identification, modeling of viscoplastic material behaviors, crack growth analysis of welded specimens, structural design, parameter estimation for nonlinear finite element analyses, and equation solver.

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