Pages that link to "Item:Q2214626"

The following pages link to Quantifying model form uncertainty in Reynolds-averaged turbulence models with Bayesian deep neural networks (Q2214626):

Displaying 34 items.

• Predictive RANS simulations via Bayesian model-scenario averaging (Q349418) (← links)
• 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)
• A data-driven adaptive Reynolds-averaged Navier-Stokes \(k\)-\(\omega\) model for turbulent flow (Q1692004) (← links)
• Retrospective cost adaptive Reynolds-averaged Navier-Stokes \(k\)-\(\omega\) model for data-driven unsteady turbulent simulations (Q1699486) (← links)
• Representation of stress tensor perturbations with application in machine-learning-assisted turbulence modeling (Q1986915) (← links)
• Quantifying turbulence model uncertainty in Reynolds-averaged Navier-Stokes simulations of a pin-fin array. I: Flow field (Q2019928) (← links)
• Mosaic flows: a transferable deep learning framework for solving PDEs on unseen domains (Q2072515) (← links)
• A Bayesian multiscale deep learning framework for flows in random media (Q2072635) (← links)
• Solving inverse problems using conditional invertible neural networks (Q2120777) (← links)
• Data-driven discovery of coarse-grained equations (Q2124010) (← links)
• Uncertainty quantification for data-driven turbulence modelling with Mondrian forests (Q2124898) (← links)
• State estimation with limited sensors -- a deep learning based approach (Q2135833) (← 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)
• Data-driven modelling of nonlinear spatio-temporal fluid flows using a deep convolutional generative adversarial network (Q2184313) (← links)
• Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data (Q2222275) (← links)
• Modeling the dynamics of PDE systems with physics-constrained deep auto-regressive networks (Q2222972) (← links)
• A turbulent eddy-viscosity surrogate modeling framework for Reynolds-averaged Navier-Stokes simulations (Q2245422) (← links)
• A random matrix approach for quantifying model-form uncertainties in turbulence modeling (Q2308784) (← links)
• Physics-informed covariance kernel for model-form uncertainty quantification with application to turbulent flows (Q2331882) (← links)
• A paradigm for data-driven predictive modeling using field inversion and machine learning (Q2374961) (← links)
• Parameter estimation with the Markov chain Monte Carlo method aided by evolutionary neural networks in a water hammer model (Q2686514) (← links)
• Enforcing Imprecise Constraints on Generative Adversarial Networks for Emulating Physical Systems (Q5163887) (← links)
• Reynolds-averaged Navier–Stokes equations with explicit data-driven Reynolds stress closure can be ill-conditioned (Q5235562) (← links)
• Reynolds averaged turbulence modelling using deep neural networks with embedded invariance (Q5360504) (← links)
• Turbulence Modeling in the Age of Data (Q5377509) (← links)
• Feature importance in neural networks as a means of interpretation for data-driven turbulence models (Q6095918) (← links)
• Uncertainty quantification analysis for simulation of wakes in wind-farms using a stochastic RANS solver, compared with a deep learning approach (Q6100092) (← links)
• Machine learning for RANS turbulence modeling of variable property flows (Q6158535) (← links)
• On the improvement of the extrapolation capability of an iterative machine-learning based RANS framework (Q6158562) (← links)
• Numerical Analysis for Convergence of a Sample-Wise Backpropagation Method for Training Stochastic Neural Networks (Q6190298) (← links)
• A probabilistic, data-driven closure model for RANS simulations with aleatoric, model uncertainty (Q6553801) (← 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)