Pages that link to "Item:Q5042008"
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The following pages link to Physics-Driven Learning of the Steady Navier-Stokes Equations using Deep Convolutional Neural Networks (Q5042008):
Displaying 22 items.
- Discretizationnet: a machine-learning based solver for Navier-Stokes equations using finite volume discretization (Q2021855) (← links)
- NSFnets (Navier-Stokes flow nets): physics-informed neural networks for the incompressible Navier-Stokes equations (Q2127017) (← links)
- Error estimates for deep learning methods in fluid dynamics (Q2149063) (← links)
- Data-driven modelling of nonlinear spatio-temporal fluid flows using a deep convolutional generative adversarial network (Q2184313) (← links)
- Physics informed by deep learning: numerical solutions of modified Korteweg-de Vries equation (Q2244291) (← links)
- ReF-nets: physics-informed neural network for Reynolds equation of gas bearing (Q2670343) (← links)
- Structure preservation for the deep neural network multigrid solver (Q2672194) (← links)
- Surrogate convolutional neural network models for steady computational fluid dynamics simulations (Q2672202) (← links)
- Bounded nonlinear forecasts of partially observed geophysical systems with physics-constrained deep learning (Q2688065) (← links)
- Theoretical prerequisites for physically justified machine learning and its applications to fluid dynamics (Q2693664) (← links)
- Flow over an espresso cup: inferring 3-D velocity and pressure fields from tomographic background oriented Schlieren via physics-informed neural networks (Q3389009) (← links)
- Deep learning of vortex-induced vibrations (Q4647380) (← links)
- Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations (Q5023414) (← links)
- Learned turbulence modelling with differentiable fluid solvers: physics-based loss functions and optimisation horizons (Q5038552) (← links)
- Learning to Discretize: Solving 1D Scalar Conservation Laws via Deep Reinforcement Learning (Q5162375) (← links)
- Deep learning in turbulent convection networks (Q5218582) (← links)
- Learning Invariant Representation of Multiscale Hyperelastic Constitutive Law from Sparse Experimental Data (Q6049615) (← links)
- Physics-informed neural networks for the Reynolds-averaged Navier-Stokes modeling of Rayleigh-Taylor turbulent mixing (Q6060732) (← links)
- Neural vortex method: from finite Lagrangian particles to infinite dimensional Eulerian dynamics (Q6100103) (← links)
- Less Emphasis on Hard Regions: Curriculum Learning of PINNs for Singularly Perturbed Convection-Diffusion-Reaction Problems (Q6192635) (← links)
- Splitting physics-informed neural networks for inferring the dynamics of integer- and fractional-order neuron models (Q6537067) (← links)
- Machine learning algorithm for the Monge-Ampère equation with transport boundary conditions (Q6630933) (← links)
