Pages that link to "Item:Q2115570"
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The following pages link to Automated constitutive modeling of isotropic hyperelasticity based on artificial neural networks (Q2115570):
Displaying 14 items.
- A new family of constitutive artificial neural networks towards automated model discovery (Q2679491) (← links)
- Automated discovery of generalized standard material models with EUCLID (Q2683452) (← links)
- Modular machine learning-based elastoplasticity: generalization in the context of limited data (Q2693407) (← links)
- (Q5852424) (← links)
- A mechanics‐informed artificial neural network approach in data‐driven constitutive modeling (Q6069980) (← links)
- A comparative study on different neural network architectures to model inelasticity (Q6082629) (← links)
- \(\mathrm{FE^{ANN}}\): an efficient data-driven multiscale approach based on physics-constrained neural networks and automated data mining (Q6101611) (← links)
- Incompressible rubber thermoelasticity: a neural network approach (Q6101617) (← links)
- Neural network-based multiscale modeling of finite strain magneto-elasticity with relaxed convexity criteria (Q6121688) (← links)
- CarNum: parallel numerical framework for computational cardiac electromechanics (Q6173758) (← links)
- Extreme sparsification of physics-augmented neural networks for interpretable model discovery in mechanics (Q6550128) (← links)
- Physics-constrained symbolic model discovery for polyconvex incompressible hyperelastic materials (Q6589318) (← links)
- Viscoelasticty with physics-augmented neural networks: model formulation and training methods without prescribed internal variables (Q6661941) (← links)
- Machine-learning-based virtual fields method: application to anisotropic hyperelasticity (Q6669068) (← links)
