Geometric deep learning for computational mechanics Part II: Graph embedding for interpretable multiscale plasticity. (arXiv:2208.00246v1 [cs.LG])

The history-dependent behaviors of classical plasticity models are often
driven by internal variables evolved according to phenomenological laws. The
difficulty to interpret how these internal variables represent a history of
deformation, the lack of direct measurement of these internal variables for
calibration and validation, and the weak physical underpinning of those
phenomenological laws have long been criticized as barriers to creating
realistic models. In this work, geometric machine learning on graph data (e.g.
finite element solutions) is used as a …

arxiv computational deep learning embedding graph ii learning lg part