Machine-learning-based virtual fields method: application to anisotropic hyperelasticity
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Publication:6669068
DOI10.1016/j.cma.2024.117580MaRDI QIDQ6669068
Ali Yousefi, Stéphane Avril, Shuangshuang Meng
Publication date: 22 January 2025
Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)
neural networkdeep learninginverse identificationhyperelastic modelsbiological tissuevirtual field method
Cites Work
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- Geometric deep learning for computational mechanics. I: Anisotropic hyperelasticity
- Introducing regularization into the virtual fields method (VFM) to identify nonhomogeneous elastic property distributions
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- Sensitivity of the virtual fields method to noisy data
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- A new constitutive framework for arterial wall mechanics and a comparative study of material models
- Advanced discretization techniques for hyperelastic physics-augmented neural networks
- Benchmarking physics-informed frameworks for data-driven hyperelasticity
- NN-mCRE: a modified constitutive relation error framework for unsupervised learning of nonlinear state laws with physics-augmented neural networks
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