One for all: Universal material model based on minimal state-space neural networks

Computational models describing the mechanical behavior of materials are indispensable when optimizing the stiffness and strength of structures. The use of state-of-the-art models is often limited in engineering practice due to their mathematical complexity, with each material class requiring its own distinct formulation. Here, we develop a recurrent neural network framework for material modeling by introducing Minimal State Cells. The framework is successfully applied to datasets representing four distinct classes of materials. It reproduces the three-dimensional stress-strain responses for arbitrary loading paths accurately and replicates the state space of conventional models. The final result is a universal model that is flexible enough to capture the mechanical behavior of any engineering material while providing an interpretable representation of their state.

Automated summary

A recurrent neural network framework with Minimal State Cells is developed for material modeling, successfully applied to datasets of four distinct classes of materials, accurately reproducing stress-strain responses for any loading path. The final result is a universal, flexible model that can capture the mechanical behavior of any engineering material while providing an interpretable representation of their state.