Pages that link to "Item:Q525932"
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The following pages link to Quantifying and reducing model-form uncertainties in Reynolds-averaged Navier-Stokes simulations: a data-driven, physics-informed Bayesian approach (Q525932):
Displaying 10 items.
- Sub-grid scale model classification and blending through deep learning (Q5379089) (← links)
- Uncertainty quantification analysis for simulation of wakes in wind-farms using a stochastic RANS solver, compared with a deep learning approach (Q6100092) (← 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)
- HiDeNN-FEM: a seamless machine learning approach to nonlinear finite element analysis (Q6159332) (← 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)
- Bayesian interface technique-based inverse estimation of closure coefficients of standard \(k-\epsilon\) turbulence model by limiting the number of DNS data points for flow over a periodic hill (Q6569724) (← links)
- Learning about structural errors in models of complex dynamical systems (Q6572173) (← links)
- Optimal sensor placement for ensemble-based data assimilation using gradient-weighted class activation mapping (Q6589890) (← links)
