Pages that link to "Item:Q2693414"
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The following pages link to Physically recurrent neural networks for path-dependent heterogeneous materials: embedding constitutive models in a data-driven surrogate (Q2693414):
Displaying 11 items.
- Constitutive artificial neural networks: a fast and general approach to predictive data-driven constitutive modeling by deep learning (Q2120033) (← links)
- Inside the black box: a physical basis for the effectiveness of deep generative models of amorphous materials (Q2133569) (← links)
- Physics-informed multi-LSTM networks for metamodeling of nonlinear structures (Q2236167) (← links)
- Deep autoencoders for physics-constrained data-driven nonlinear materials modeling (Q2237774) (← links)
- Data-driven spatiotemporal modeling for structural dynamics on irregular domains by stochastic dependency neural estimation (Q2678544) (← links)
- Surrogate modeling for the homogenization of elastoplastic composites based on RBF interpolation (Q6096506) (← links)
- Embedding physical knowledge in deep neural networks for predicting the phonon dispersion curves of cellular metamaterials (Q6159334) (← links)
- Physically recurrent neural networks for path-dependent heterogeneous materials: embedding constitutive models in a data-driven surrogate (Q6410876) (← links)
- Micromechanics-based deep-learning for composites: challenges and future perspectives (Q6540411) (← links)
- Recurrent neural networks and transfer learning for predicting elasto-plasticity in woven composites (Q6586368) (← links)
- Learning solutions of thermodynamics-based nonlinear constitutive material models using physics-informed neural networks (Q6604129) (← links)
