ArticleComputer methods in applied mechanics and engineering2023
Data-driven anisotropic finite viscoelasticity using neural ordinary differential equations.
Article in Computer methods in applied mechanics and engineering, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Article
- Monotone Peridynamic Neural Operator for Nonlinear Material Modeling with Conditionally Unique Solutions.Computer methods in applied mechanics and engineering · 2026Article
- LLM-driven design of physics-constrained constitutive models: Two agents are better than one.Machine learning for computational science and engineering · 2026Article
- Generative Hyperelasticity with Physics-Informed Probabilistic Diffusion Fields.Engineering with computers · 2025Article
- Biaxial testing and sensory texture evaluation of plant-based and animal deli meat.Current research in food science · 2025Article
- Data-driven continuum damage mechanics with built-in physics.Extreme Mechanics Letters · 2024Article
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Authors and funding
4 authors.
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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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