Discovering the mechanics of artificial and real meat
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Publication:6096485
DOI10.1016/j.cma.2023.116236MaRDI QIDQ6096485
Ellen Kuhl, Skyler R. St. Pierre, Ethan C. Darwin, Divya Rajasekharan, Marc E. Levenston, Kevin Linka
Publication date: 12 September 2023
Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)
constitutive modelingOgden modelautomated model discoveryconstitutive artificial neural networksValanis-Landel modelartificial meat
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Cites Work
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- A view on anisotropic finite hyperelasticity
- Model-free data-driven computational mechanics enhanced by tensor voting
- A mixed formulation for physics-informed neural networks as a potential solver for engineering problems in heterogeneous domains: comparison with finite element method
- Constitutive artificial neural networks: a fast and general approach to predictive data-driven constitutive modeling by deep learning
- On physics-informed data-driven isotropic and anisotropic constitutive models through probabilistic machine learning and space-filling sampling
- Data-driven tissue mechanics with polyconvex neural ordinary differential equations
- Unsupervised discovery of interpretable hyperelastic constitutive laws
- A new family of constitutive artificial neural networks towards automated model discovery
- Large deformation isotropic elasticity – on the correlation of theory and experiment for incompressible rubberlike solids
- Large elastic deformations of isotropic materials IV. further developments of the general theory
- Parameter estimation of hyperelasticity relations of generalized polynomial-type with constraint conditions
- A mechanics‐informed artificial neural network approach in data‐driven constitutive modeling
- Automated model discovery for skin: discovering the best model, data, and experiment
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