Machine learning-assisted parameter identification for constitutive models based on concatenated loading path sequences
A hybrid strategy for the identification of material parameters of constitutive models is presented. One main challenge in the context of classic optimisation-based parameter identification schemes is the generation of adequate starting values for the multi-objective optimisation procedure. We address this issue by employing an artificial neural network that is trained with data obtained from the
