Pages that link to "Item:Q2033662"
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The following pages link to Anisotropic hyperelastic constitutive models for finite deformations combining material theory and data-driven approaches with application to cubic lattice metamaterials (Q2033662):
Displaying 17 items.
- Finite electro-elasticity with physics-augmented neural networks (Q2083132) (← links)
- A mixed formulation for physics-informed neural networks as a potential solver for engineering problems in heterogeneous domains: comparison with finite element method (Q2096848) (← links)
- Learning constitutive models from microstructural simulations via a non-intrusive reduced basis method: extension to geometrical parameterizations (Q2096859) (← links)
- Automated constitutive modeling of isotropic hyperelasticity based on artificial neural networks (Q2115570) (← links)
- Bayesian-EUCLID: discovering hyperelastic material laws with uncertainties (Q2160432) (← links)
- Numerical approaches for investigating quasiconvexity in the context of Morrey's conjecture (Q2171039) (← links)
- Model-data-driven constitutive responses: application to a multiscale computational framework (Q2234818) (← links)
- Nonlinear multiscale simulation of elastic beam lattices with anisotropic homogenized constitutive models based on artificial neural networks (Q2667309) (← links)
- Enhancing phenomenological yield functions with data: challenges and opportunities (Q2692822) (← links)
- Material modeling for parametric, anisotropic finite strain hyperelasticity based on machine learning with application in optimization of metamaterials (Q6061746) (← links)
- Advanced discretization techniques for hyperelastic physics-augmented neural networks (Q6062433) (← links)
- A comparative study on different neural network architectures to model inelasticity (Q6082629) (← links)
- Spiking recurrent neural networks for neuromorphic computing in nonlinear structural mechanics (Q6097653) (← links)
- \(\mathrm{FE^{ANN}}\): an efficient data-driven multiscale approach based on physics-constrained neural networks and automated data mining (Q6101611) (← links)
- Physics-based self-learning spiking neural network enhanced time-integration scheme for computing viscoplastic structural finite element response (Q6125507) (← links)
- NN-mCRE: a modified constitutive relation error framework for unsupervised learning of nonlinear state laws with physics-augmented neural networks (Q6499924) (← links)
- A thermodynamically consistent physics-informed deep learning material model for short fiber/polymer nanocomposites (Q6557800) (← links)
