Efficient order reduction for the prediction of macroscopic mechanical properties of nonlinear fiber-reinforced composite material with varying design parameters

This PhD aims to develop model order reduction techniques that will enable the efficient parametric studies of fiber-reinforced composite materials to predict its mechanical behavior. Proper orthogonal decomposition approach will be used to reduce the number of variables to be solved in the multiscale strength simulations. Hyper-reduction techniques will then be applied to reduce the nonlinear function evaluation cost for complex material constituents.