Pages that link to "Item:Q2331882"
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The following pages link to Physics-informed covariance kernel for model-form uncertainty quantification with application to turbulent flows (Q2331882):
Displaying 9 items.
- Quantifying and reducing model-form uncertainties in Reynolds-averaged Navier-Stokes simulations: a data-driven, physics-informed Bayesian approach (Q525932) (← links)
- Bayesian uncertainty quantification of turbulence models based on high-order adjoint (Q1645947) (← links)
- Enforcing boundary conditions on physical fields in Bayesian inversion (Q2186856) (← links)
- A turbulent eddy-viscosity surrogate modeling framework for Reynolds-averaged Navier-Stokes simulations (Q2245422) (← links)
- Ensemble Kalman method for learning turbulence models from indirect observation data (Q5038553) (← links)
- DAFI: An Open-Source Framework for Ensemble-Based Data Assimilation and Field Inversion (Q5163237) (← links)
- Combining direct and indirect sparse data for learning generalizable turbulence models (Q6107115) (← links)
- Evaluation of physics constrained data-driven methods for turbulence model uncertainty quantification (Q6158540) (← links)
- Optimal sensor placement for ensemble-based data assimilation using gradient-weighted class activation mapping (Q6589890) (← links)
