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• W4366608160 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 human brain tissue, blood clots, natural rubber and human myocardium and show that the data-driven method outperforms traditional, closed-form models of viscoelasticity." @default.
• W4366608160 created "2023-04-23" @default.
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• W4366608160 date "2023-06-01" @default.
• W4366608160 modified "2023-10-16" @default.
• W4366608160 title "Data-driven anisotropic finite viscoelasticity using neural ordinary differential equations" @default.
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• W4366608160 doi "https://doi.org/10.1016/j.cma.2023.116046" @default.
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