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Data-driven turbulence modelling approaches are gaining increasing interest from the CFD community.
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J. C. Hunt, A. M. Savill, Guidelines and criteria for the use of turbulence models in complex flows, 2005. doi: 10.1017/CBO9780511543227.008
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Highly resolved large-eddy simulation of separated flow in a channel with streamwise periodic constrictions,
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Aerodynamics, computers and the environment,
P. G. Tucker, J. R. DeBonis, · 2013
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Trends in turbomachinery turbulence treatments,
P. G. Tucker, · 2013
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Gaussian processes for big data,
J. Hensman, N. Fusi, N. D. Lawrence, · 2013
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Large Eddy Simulation of wind farm aerodynamics: A review,
D. Mehta, A. H. van Zuijlen, B. Koren, J. G. Holierhoek, H. Bijl, · 2014
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Confidence intervals for random forests: The jackknife and the infinitesimal jackknife,
S. Wager, T. Hastie, B. Efron, · 2014
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P. Tucker, Unsteady Computational Fluid Dynamics in Aeronautics, Springer Netherlands, 2014. doi: 10.1007/978-94-007-7049-2
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High-Performance High-Lift Design for Laminar Wings,
J. Wild, · 2015
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Simple and scalable predictive uncertainty estimation using deep ensembles,
B. Lakshminarayanan, A. Pritzel, C. Blundell, · 2017
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A unified approach to interpreting model predictions,
S. M. Lundberg, S.-I. Lee, · 2017
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LES over RANS in building simulation for outdoor and indoor applications: A foregone conclusion?,
B. Blocken, · 2018
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Physics-informed machine learning approach for augmenting turbulence models: A comprehensive framework,
J. L. Wu, H. Xiao, E. Paterson, · 2018
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Quantification of Model Uncertainty in RANS Simulations: A Review,
H. Xiao, P. Cinnella, · 2018
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Simulation and adjoint-based design for variable density incompressible flows with heat transfer,
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Evaluation of machine learning algorithms for prediction of regions of high Reynolds averaged Navier Stokes uncertainty,
J. Ling, J. Templeton, · 2015
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A paradigm for data-driven predictive modeling using field inversion and machine learning,
E. J. Parish, K. Duraisamy, · 2015
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Particle gibbs for bayesian additive regression trees,
B. Lakshminarayanan, D. Roy, Y. W. Teh, · 2015
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Large Eddy Simulations of convergent–divergent channel flows at moderate Reynolds numbers,
L. A. Schiavo, A. B. Jesus, J. L. Azevedo, W. R. Wolf, · 2015
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L. Raynal, F. Augier, F. Bazer-Bachi, Y. Haroun, C. Pereira Da Fonte, CFD Applied to Process Development in the Oil and Gas Industry - A Review, 2016. doi: 10.2516/ogst/2015019
2016
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Reynolds averaged turbulence modelling using deep neural networks with embedded invariance,
J. Ling, A. Kurzawski, J. Templeton, · 2016
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T. D. Economon, · 2018
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R. Vinuesa, P. S. Negi, M. Atzori, A. Hanifi, D. S. Henningson, P. Schlatter, Turbulent boundary layers around wing sections up to Rec=1,000,000, 2018. doi: 10.1016/j.ijheatfluidflow.2018.04.017
2018
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Large Eddy Simulation of Boundary Layer Transition Mechanisms in a Gas-Turbine Compressor Cascade,
A. D. Scillitoe, P. G. Tucker, P. Adami, · 2019
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Turbulence Modeling in the Age of Data,
K. Duraisamy, G. Iaccarino, H. Xiao, · 2019
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Quantifying model form uncertainty in Reynolds-averaged turbulence models with Bayesian deep neural networks,
N. Geneva, N. Zabaras, · 2019
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C. A. Blauw, R. P. Dwight, Bayesian Additive Regression Trees for data-driven RANS turbulence modelling, Ph.D. thesis, Delft University of Technology, 2019
2019
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Reynolds-averaged Navier-Stokes equations with explicit data-driven Reynolds stress closure can be ill-conditioned,
J. Wu, H. Xiao, R. Sun, Q. Wang, · 2019
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Epistemic uncertainty quantification for Reynolds-averaged Navier-Stokes modeling of separated flows over streamlined surfaces,
C. Gorlé, S. Zeoli, M. Emory, J. Larsson, G. Iaccarino, · 2019
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AMF: Aggregated Mondrian Forests for Online Learning,
J. Mourtada, S. Gaïffas, E. Scornet, · 2019
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Data-driven modelling of the reynolds stress tensor using random forests with invariance,
M. L. Kaandorp, R. P. Dwight, · 2020
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From local explanations to global understanding with explainable AI for trees,
S. M. Lundberg, G. Erion, H. Chen, A. DeGrave, J. M. Prutkin, B. Nair, R. Katz, J. Himmelfarb, N. Bansal, S.-I. Lee, · 2020
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