Model reduction techniques in computational materials mechanics

Multi-scale analyses, stochastic approaches and identification techniques represent research topics of growing interest in computational materials mechanics. In this context, it is often required to simulate complex situations, involving coupled phenomena and several nonlinearity sources. On the other hand, it may be needed to repeat the numerical analyses several times, with only a few varying parameters among those defining the problem of interest. Techniques aiming at exploiting the expected correlation between the different outputs are therefore particularly sought, in order to reduce the overall computational burden without a significant decay of the accuracy of the collected results. The present paper summarizes some computational experience in the field.