Pages that link to "Item:Q1869302"

The following pages link to Neural networks based subgrid scale modeling in large eddy simulations (Q1869302):

Displaying 34 items.

• POD based reconstruction of subgrid stresses for wall bounded flows using neural networks (Q943909) (← links)
• Efficient optimisation procedure for design problems in fluid mechanics (Q2014850) (← links)
• Seismic design value evaluation based on checking records and site geological conditions using artificial neural networks (Q2015258) (← links)
• Resampling with neural networks for stochastic parameterization in multiscale systems (Q2077663) (← links)
• Frame invariant neural network closures for Kraichnan turbulence (Q2111612) (← links)
• Spatiotemporally dynamic implicit large eddy simulation using machine learning classifiers (Q2115516) (← links)
• The neural network shifted-proper orthogonal decomposition: a machine learning approach for non-linear reduction of hyperbolic equations (Q2138717) (← links)
• Stable \textit{a posteriori} LES of 2D turbulence using convolutional neural networks: backscattering analysis and generalization to higher \(Re\) via transfer learning (Q2139011) (← links)
• Reducing data-driven dynamical subgrid scale models by physical constraints (Q2176859) (← links)
• NPLIC: a machine learning approach to piecewise linear interface construction (Q2245365) (← links)
• Subgrid-scale model for large-eddy simulation of isotropic turbulent flows using an artificial neural network (Q2334461) (← links)
• Machine learning for vortex induced vibration in turbulent flow (Q2670071) (← links)
• A machine learning framework for LES closure terms (Q2672196) (← links)
• Invariant data-driven subgrid stress modeling in the strain-rate eigenframe for large eddy simulation (Q2674143) (← links)
• Subgrid modelling for two-dimensional turbulence using neural networks (Q4559252) (← links)
• Prediction of turbulent heat transfer using convolutional neural networks (Q4972211) (← links)
• Kinetic-energy-flux-constrained model using an artificial neural network for large-eddy simulation of compressible wall-bounded turbulence (Q5015138) (← links)
• A physics-inspired alternative to spatial filtering for large-eddy simulations of turbulent flows (Q5022972) (← links)
• A Priori Sub-grid Modelling Using Artificial Neural Networks (Q5030438) (← links)
• Learned turbulence modelling with differentiable fluid solvers: physics-based loss functions and optimisation horizons (Q5038552) (← links)
• Interpreting neural network models of residual scalar flux (Q5144568) (← links)
• A neural network approach for the blind deconvolution of turbulent flows (Q5231546) (← links)
• Sub-grid scale model classification and blending through deep learning (Q5379089) (← links)
• Neural network-based modelling of subsonic cavity flows (Q5402730) (← links)
• Toward neural-network-based large eddy simulation: application to turbulent channel flow (Q5856568) (← links)
• A unified understanding of scale-resolving simulations and near-wall modelling of turbulent flows using optimal finite-element projections (Q5871608) (← links)
• Artificial-neural-network-based nonlinear algebraic models for large-eddy simulation of compressible wall-bounded turbulence (Q5886405) (← links)
• A perspective on machine learning methods in turbulence modeling (Q6068270) (← links)
• Variational multiscale super‐resolution: A data‐driven approach for reconstruction and predictive modeling of unresolved physics (Q6082595) (← links)
• Data-driven wall modeling for turbulent separated flows (Q6158122) (← links)
• Learning subgrid-scale models with neural ordinary differential equations (Q6160037) (← links)
• Optimising subgrid-scale closures for spectral energy transfer in turbulent flows (Q6193193) (← links)
• A data-driven memory model for solving turbulent flows with the pseudo-direct numerical simulation method (Q6537403) (← links)
• A recursive neural-network-based subgrid-scale model for large eddy simulation: application to homogeneous isotropic turbulence (Q6659610) (← links)