Data for "One for All: Universal Material Model Based on Minimal State-Space Neural Networks"

This repository is a companion to the article:

"One for All: Universal Material Model Based on Minimal State-Space Neural Networks"

If you use this dataset, please cite it accordingly.

Abstract:

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 3D 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.