Pages that link to "Item:Q2096901"
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The following pages link to Physics-based self-learning recurrent neural network enhanced time integration scheme for computing viscoplastic structural finite element response (Q2096901):
Displaying 12 items.
- An intelligent nonlinear meta element for elastoplastic continua: deep learning using a new time-distributed residual U-net architecture (Q2184471) (← links)
- Recurrent neural networks (RNNs) learn the constitutive law of viscoelasticity (Q2241874) (← links)
- Learning viscoelasticity models from indirect data using deep neural networks (Q2246355) (← links)
- A neural network tool for identifying the material parameters of a finite deformation viscoplasticity model with static recovery (Q5956800) (← links)
- Recurrent and convolutional neural networks in structural dynamics: a modified attention steered encoder-decoder architecture versus LSTM versus GRU versus TCN topologies to predict the response of shock wave-loaded plates (Q6084770) (← links)
- A machine learning-based viscoelastic-viscoplastic model for epoxy nanocomposites with moisture content (Q6096512) (← links)
- Spiking recurrent neural networks for neuromorphic computing in nonlinear structural mechanics (Q6097653) (← links)
- Adaptive task decomposition physics-informed neural networks (Q6120149) (← links)
- Physics-based self-learning spiking neural network enhanced time-integration scheme for computing viscoplastic structural finite element response (Q6125507) (← links)
- Transfer learning of recurrent neural network‐based plasticity models (Q6148497) (← links)
- Deep neural operator for learning transient response of interpenetrating phase composites subject to dynamic loading (Q6164292) (← links)
- A generative learning and graph-based framework for computing field variables in finite element simulations (Q6566095) (← links)
