Artificial Neural Networks in numerical modelling of composites

In this paper, we show some different concepts for the use of Artificial Neural Networks in modelling of composites and hierarchical structures. Starting from a relatively small set of suitable numerical experiments performed on a unit cell, a proper set of corresponding input-output data is created to train the network to identify the effective properties. Furthermore, ANN based procedures can be exploited in a multiscale analysis as a tool for the stress-strain recovery at lower levels of the hierarchical structure and/or to estimate the state of yielding of the materials. This kind of application is of great computational importance, since with material non-linearity they allow for a significantly improved computational efficiency. Finally, ANNs may be used to define the homogenised properties for a class of parameterised unit cells or when material characteristics depend upon a parameter (e.g. temperature, damage, etc.). The problem of the best ANN (or sufficiently good ANN) for each type of applications is discussed by means of the examples presented throughout the paper.