Artificial Neural Network forecasting for monomorphic and polymorphic uncertainty models and comparison with experimental investigations

Tools from probability or possibility in terms of fuzzy sets are usable for the description and quantification of uncertainties within numerical simulations. In this work, different monomorphic and polymorphic uncertainty models are applied on linear elastostatic structures with non‐periodic perforations in order to analyze the individual expressiveness. The first principal stress is used as an indicator for structural failure which is evaluated and classified. Artificial Neural Networks are used as surrogate model to compare and assess the uncertainty models with regard to the numerical predictions. Real experiments of perforated plates under uniaxial tension are validated with the help of various uncertainty models.