Pages that link to "Item:Q5360504"

The following pages link to Reynolds averaged turbulence modelling using deep neural networks with embedded invariance (Q5360504):

Displaying 50 items.

• RANS turbulence model development using CFD-driven machine learning (Q777616) (← links)
• Diagnostics for eddy viscosity models of turbulence including data-driven/neural network based parameterizations (Q780022) (← links)
• Learning constitutive relations from indirect observations using deep neural networks (Q781968) (← links)
• A CNN-based shock detection method in flow visualization (Q1739782) (← links)
• Neural network closures for nonlinear model order reduction (Q1756917) (← links)
• Representation of stress tensor perturbations with application in machine-learning-assisted turbulence modeling (Q1986915) (← links)
• DGM: a deep learning algorithm for solving partial differential equations (Q2002333) (← links)
• A supervised neural network for drag prediction of arbitrary 2D shapes in laminar flows at low Reynolds number (Q2019945) (← links)
• Deep learned finite elements (Q2021024) (← links)
• A machine-learning minimal-residual (ML-MRes) framework for goal-oriented finite element discretizations (Q2034897) (← links)
• Frame-independent vector-cloud neural network for nonlocal constitutive modeling on arbitrary grids (Q2060111) (← links)
• Data-driven RANS closures for wind turbine wakes under neutral conditions (Q2072344) (← links)
• Mosaic flows: a transferable deep learning framework for solving PDEs on unseen domains (Q2072515) (← links)
• Normalization effects on shallow neural networks and related asymptotic expansions (Q2072629) (← links)
• Assessments of epistemic uncertainty using Gaussian stochastic weight averaging for fluid-flow regression (Q2083714) (← links)
• Three-dimensional realizations of flood flow in large-scale rivers using the neural fuzzy-based machine-learning algorithms (Q2084088) (← links)
• Data-driven multi-grid solver for accelerated pressure projection (Q2084099) (← links)
• Towards high-accuracy deep learning inference of compressible flows over aerofoils (Q2108599) (← links)
• Frame invariant neural network closures for Kraichnan turbulence (Q2111612) (← links)
• Spatiotemporally dynamic implicit large eddy simulation using machine learning classifiers (Q2115516) (← links)
• Data-driven predictions of the Lorenz system (Q2115546) (← links)
• DPM: a deep learning PDE augmentation method with application to large-eddy simulation (Q2123852) (← links)
• Application of gene expression programming to a-posteriori LES modeling of a Taylor Green vortex (Q2123916) (← links)
• Uncertainty quantification for data-driven turbulence modelling with Mondrian forests (Q2124898) (← links)
• NSFnets (Navier-Stokes flow nets): physics-informed neural networks for the incompressible Navier-Stokes equations (Q2127017) (← links)
• Metric-based, goal-oriented mesh adaptation using machine learning (Q2127024) (← links)
• PFNN: a penalty-free neural network method for solving a class of second-order boundary-value problems on complex geometries (Q2128373) (← links)
• Customized data-driven RANS closures for bi-fidelity LES-RANS optimization (Q2128496) (← links)
• Physics-inspired architecture for neural network modeling of forces and torques in particle-laden flows (Q2129550) (← links)
• DeepM\&Mnet: inferring the electroconvection multiphysics fields based on operator approximation by neural networks (Q2131084) (← links)
• Deep-learning accelerated calculation of real-fluid properties in numerical simulation of complex flowfields (Q2132659) (← links)
• Multi-objective CFD-driven development of coupled turbulence closure models (Q2133604) (← links)
• Simple computational strategies for more effective physics-informed neural networks modeling of turbulent natural convection (Q2133780) (← links)
• \(S\)-frame discrepancy correction models for data-informed Reynolds stress closure (Q2134488) (← links)
• Machine learning for fluid flow reconstruction from limited measurements (Q2134510) (← links)
• CFD-driven symbolic identification of algebraic Reynolds-stress models (Q2135797) (← links)
• State estimation with limited sensors -- a deep learning based approach (Q2135833) (← links)
• Explicit physics-informed neural networks for nonlinear closure: the case of transport in tissues (Q2136464) (← links)
• RotEqNet: rotation-equivariant network for fluid systems with symmetric high-order tensors (Q2138017) (← 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)
• A robust super-resolution reconstruction model of turbulent flow data based on deep learning (Q2139577) (← links)
• IGA-reuse-NET: a deep-learning-based isogeometric analysis-reuse approach with topology-consistent parameterization (Q2139715) (← links)
• The Fermi-Pasta-Ulam-Tsingou recurrence for discrete systems: cascading mechanism and machine learning for the Ablowitz-Ladik equation (Q2160956) (← links)
• A data-driven shock capturing approach for discontinuous Galerkin methods (Q2166588) (← links)
• Flows over periodic hills of parameterized geometries: a dataset for data-driven turbulence modeling from direct simulations (Q2176735) (← links)
• Bayesian model-scenario averaged predictions of compressor cascade flows under uncertain turbulence models (Q2176864) (← links)
• Data-driven modelling of the Reynolds stress tensor using random forests with invariance (Q2180004) (← links)
• A deep learning perspective on predicting permeability in porous media from network modeling to direct simulation (Q2192831) (← links)
• An artificial neural network framework for reduced order modeling of transient flows (Q2206568) (← links)
• Quantifying model form uncertainty in Reynolds-averaged turbulence models with Bayesian deep neural networks (Q2214626) (← links)