AI Article Synopsis
- Developed a data-driven model for anisotropic finite viscoelasticity using neural ordinary differential equations, ensuring compliance with physics-based constraints.
- This method allows for modeling viscoelastic behavior of materials under complex loading conditions in 3D, even with significant deformations.
- The model was trained on stress-strain data from various materials, demonstrating better performance than traditional models of viscoelasticity.
Article Abstract
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.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10327622 | PMC |
| http://dx.doi.org/10.1016/j.cma.2023.116046 | DOI Listing |
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