Hybrid Data-Driven and Physics-Informed Learning of Cyclic Plasticity with Neural Networks

The authors propose an efficient and explainable Machine Learning approach to represent cyclic plasticity, achieving high accuracy and stability. The model architecture is simpler and more efficient compared to existing solutions, validated for accuracy and stability.