Pages that link to "Item:Q2180004"
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The following pages link to Data-driven modelling of the Reynolds stress tensor using random forests with invariance (Q2180004):
Displaying 26 items.
- Machine learning strategies for systems with invariance properties (Q726815) (← links)
- A novel evolutionary algorithm applied to algebraic modifications of the RANS stress-strain relationship (Q1685075) (← links)
- A data-driven adaptive Reynolds-averaged Navier-Stokes \(k\)-\(\omega\) model for turbulent flow (Q1692004) (← links)
- Representation of stress tensor perturbations with application in machine-learning-assisted turbulence modeling (Q1986915) (← links)
- Data-driven RANS closures for wind turbine wakes under neutral conditions (Q2072344) (← links)
- Uncertainty quantification for data-driven turbulence modelling with Mondrian forests (Q2124898) (← links)
- Customized data-driven RANS closures for bi-fidelity LES-RANS optimization (Q2128496) (← links)
- Multi-objective CFD-driven development of coupled turbulence closure models (Q2133604) (← links)
- \(S\)-frame discrepancy correction models for data-informed Reynolds stress closure (Q2134488) (← 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)
- Flows over periodic hills of parameterized geometries: a dataset for data-driven turbulence modeling from direct simulations (Q2176735) (← links)
- Quantifying model form uncertainty in Reynolds-averaged turbulence models with Bayesian deep neural networks (Q2214626) (← links)
- Data-driven RANS closures for three-dimensional flows around bluff bodies (Q2245405) (← links)
- The use of the Reynolds force vector in a physics informed machine learning approach for predictive turbulence modeling (Q2333058) (← links)
- Conditioning and accurate solutions of Reynolds average Navier–Stokes equations with data-driven turbulence closures (Q3388856) (← links)
- Frame Invariance and Scalability of Neural Operators for Partial Differential Equations (Q5106293) (← links)
- On the Generalizability of Machine-Learning-Assisted Anisotropy Mappings for Predictive Turbulence Modelling (Q5880411) (← links)
- A highly accurate strategy for data-driven turbulence modeling (Q6125393) (← links)
- Machine learning for RANS turbulence modeling of variable property flows (Q6158535) (← links)
- Enhancement of RANS models by means of the tensor basis random forest for turbulent flows in two-dimensional channels with bumps (Q6552836) (← links)
- A probabilistic, data-driven closure model for RANS simulations with aleatoric, model uncertainty (Q6553801) (← links)
- Data-driven approach for modeling Reynolds stress tensor with invariance preservation (Q6566931) (← links)
- Revisiting tensor basis neural network for Reynolds stress modeling: application to plane channel and square duct flows (Q6566970) (← links)
- Local equilibrium approach in the problem of the dynamics of a plane turbulent wake in a passively stratified medium (Q6586502) (← links)
- Neural operator based Reynolds averaged turbulence modelling (Q6647989) (← links)
- A data-driven turbulence modeling for the Reynolds stress tensor transport equation (Q6659842) (← links)
