Pages that link to "Item:Q2241874"
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The following pages link to Recurrent neural networks (RNNs) learn the constitutive law of viscoelasticity (Q2241874):
Displaying 13 items.
- Learning constitutive relations from indirect observations using deep neural networks (Q781968) (← links)
- Recurrent neural networks (RNNs) with dimensionality reduction and break down in computational mechanics; application to multi-scale localization step (Q2072735) (← links)
- Physics-based self-learning recurrent neural network enhanced time integration scheme for computing viscoplastic structural finite element response (Q2096901) (← links)
- Mechanistically informed data-driven modeling of cyclic plasticity via artificial neural networks (Q2138793) (← links)
- Learning viscoelasticity models from indirect data using deep neural networks (Q2246355) (← links)
- Thermodynamically consistent machine-learned internal state variable approach for data-driven modeling of path-dependent materials (Q2679297) (← links)
- Physically recurrent neural networks for path-dependent heterogeneous materials: embedding constitutive models in a data-driven surrogate (Q2693414) (← links)
- Deep energy method in topology optimization applications (Q2694685) (← links)
- Modelling of shearing behaviour of a residual soil with Recurrent Neural Network (Q4222652) (← links)
- Data-driven anisotropic finite viscoelasticity using neural ordinary differential equations (Q6097591) (← links)
- Transfer learning of recurrent neural network‐based plasticity models (Q6148497) (← links)
- A framework for neural network based constitutive modelling of inelastic materials (Q6194141) (← links)
- Viscoelastic constitutive artificial neural networks (vCANNs) -- a framework for data-driven anisotropic nonlinear finite viscoelasticity (Q6196602) (← links)
