Accelerating Elasto-Plastic Finite Element Simulations Using a Neuber-Type Plastic Correction Algorithm and Machine Learning

This paper introduces a new local plastic correction algorithm that accelerates elasto-plastic finite element simulations for structural problems exhibiting localized plasticity. The proposed method belongs to the category of generalized multi-axial Neuber-type methods, which process the results of an elastic prediction point-wise to calculate an approximation of the full elasto-plastic solution. The authors also show that the proposed local plastic correction algorithm can be further accelerated by employing a simple meta-modelling strategy, with virtually no added errors. Additionally, a deep-learning-based corrective layer is developed to improve the approximation accuracy of the plastic corrector.