A data-efficient approach to cyclic plasticity modeling via physics-augmented neural networks

Formulating constitutive models for plasticity presents significant challenges. In particular, cyclic inelastic analysis is computationally intensive, making machine learning a promising tool for capturing complex material behavior with reduced computational cost and enabling near real-time execution after training. This study builds upon a hybrid data-efficient methodology for modeling the cyclic plasticity of metals, which integrates data-driven training with physics-augmented regularization using a fully connected neural network (FCNN). We propose an enhancement to this FCNN framework aimed at improving its efficiency and enabling it to serve as a replacement for conventional models based on radial return mapping (RRM). By selecting input and target variables analogous to those used in RRM, the network’s behavior becomes interpretable—grounded in explicitly defined quantities rather than opaque internal states. Notably, kinematic hardening is modeled without the use of backstress variables as explicit inputs, allowing the model to rely solely on data obtainable from standard laboratory tests—a key innovation of this study. Through physics-augmented regularization, the data-efficient approach achieves high accuracy and numerical stability with relatively limited training data, significantly improving computational performance. Further efficiency gains are realized by optimizing the network architecture, reducing the number of input variables, and refining the data pre- and post-processing steps. The proposed enhancements result in a 15% reduction in training time while maintaining accuracy on par with the original NN model.