Pages that link to "Item:Q2132659"
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The following pages link to Deep-learning accelerated calculation of real-fluid properties in numerical simulation of complex flowfields (Q2132659):
Displaying 13 items.
- DeepM\&Mnet for hypersonics: predicting the coupled flow and finite-rate chemistry behind a normal shock using neural-network approximation of operators (Q2133505) (← links)
- A class of structurally complete approximate Riemann solvers for trans- and supercritical flows with large gradients (Q2168336) (← links)
- Physics-informed neural networks for high-speed flows (Q2175317) (← links)
- Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data (Q2176917) (← links)
- A review on deep reinforcement learning for fluid mechanics (Q2245392) (← links)
- Multi-Scale Deep Neural Network (MscaleDNN) Methods for Oscillatory Stokes Flows in Complex Domains (Q5162374) (← links)
- A combination of large eddy simulation and physics-informed machine learning to predict pore-scale flow behaviours in fibrous porous media: a case study of transient flow passing through a surgical mask (Q6043958) (← links)
- Fluid Flow Modelling Using Physics-Informed Convolutional Neural Network in Parametrised Domains (Q6092912) (← links)
- FluxNet: a physics-informed learning-based Riemann solver for transcritical flows with non-ideal thermodynamics (Q6097610) (← links)
- A novel temperature prediction method without using energy equation based on physics-informed neural network (PINN): a case study on plate-circular/square pin-fin heat sinks (Q6138011) (← links)
- A TensorFlow simulation framework for scientific computing of fluid flows on tensor processing units (Q6159635) (← links)
- Accelerating hypersonic reentry simulations using deep learning-based hybridization (with guarantees) (Q6187668) (← links)
- A surrogate model based on deep convolutional neural networks for solving deformation caused by moisture diffusion (Q6540139) (← links)
