Pages that link to "Item:Q6055145"

The following pages link to Physics-incorporated convolutional recurrent neural networks for source identification and forecasting of dynamical systems (Q6055145):

Displaying 8 items.

• Multi-level convolutional autoencoder networks for parametric prediction of spatio-temporal dynamics (Q2020980) (← links)
• Physical representation learning and parameter identification from video using differentiable physics (Q2117001) (← links)
• Prediction and identification of physical systems by means of physically-guided neural networks with meaningful internal layers (Q2236964) (← links)
• Bayesian physics informed neural networks for real-world nonlinear dynamical systems (Q2679296) (← links)
• Bounded nonlinear forecasts of partially observed geophysical systems with physics-constrained deep learning (Q2688065) (← links)
• Deep learning-accelerated computational framework based on physics informed neural network for the solution of linear elasticity (Q6053463) (← links)
• \(\mathrm{U}^p\)-net: a generic deep learning-based time stepper for parameterized spatio-temporal dynamics (Q6159312) (← links)
• A deep learning modeling framework to capture mixing patterns in reactive-transport systems (Q6358062) (← links)