We develop a fully data-driven model of anisotropic finite viscoelasticity using neural ordinary differential equations as building blocks. We replace the Helmholtz free energy function and the dissipation potential with data-driven functions that a priori satisfy physics-based constraints such as objectivity and the second law of thermodynamics. Our approach enables modeling viscoelastic behavior of materials under arbitrary loads in three-dimensions even with large deformations and large deviations from the thermodynamic equilibrium. The data-driven nature of the governing potentials endows the model with much needed flexibility in modeling the viscoelastic behavior of a wide class of materials. We train the model using stress-strain data from biological and synthetic materials including humain brain tissue, blood clots, natural rubber and human myocardium and show that the data-driven method outperforms traditional, closed-form models of viscoelasticity.
@article{arxiv.2302.03598,
title = {Data-driven anisotropic finite viscoelasticity using neural ordinary differential equations},
author = {Vahidullah Tac and Manuel K. Rausch and Francisco Sahli-Costabal and Adrian B. Tepole},
journal= {arXiv preprint arXiv:2302.03598},
year = {2023}
}