Pages that link to "Item:Q1645947"
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The following pages link to Bayesian uncertainty quantification of turbulence models based on high-order adjoint (Q1645947):
Displaying 11 items.
- Quantifying and reducing model-form uncertainties in Reynolds-averaged Navier-Stokes simulations: a data-driven, physics-informed Bayesian approach (Q525932) (← links)
- An efficient approach for quantifying parameter uncertainty in the SST turbulence model (Q667443) (← links)
- Regularized ensemble Kalman methods for inverse problems (Q781982) (← links)
- An efficient Bayesian uncertainty quantification approach with application to \(k\)-\(\omega\)-\(\gamma\) transition modeling (Q1645433) (← links)
- Bayesian calibration of the constants of the \(k-\varepsilon\) turbulence model for a CFD model of street canyon flow (Q1668426) (← links)
- Gradient-based methods for uncertainty quantification in hypersonic flows (Q2015969) (← links)
- Evaluation of ensemble methods for quantifying uncertainties in steady-state CFD applications with small ensemble sizes (Q2180033) (← links)
- Determining \textit{a priori} a RANS model's applicable range via global epistemic uncertainty quantification (Q2245534) (← links)
- Estimating parameter and discretization uncertainties using a laminar-turbulent transition model (Q2245551) (← links)
- Physics-informed covariance kernel for model-form uncertainty quantification with application to turbulent flows (Q2331882) (← links)
- A Sparse Grid Method for Bayesian Uncertainty Quantification with Application to Large Eddy Simulation Turbulence Models (Q2808028) (← links)
