Evaluation of machine learning algorithms for prediction of regions of high Reynolds averaged Navier Stokes uncertainty
暂无分享,去创建一个
[1] P. Mahalanobis. On the generalized distance in statistics , 1936 .
[2] B. Launder,et al. Progress in the development of a Reynolds-stress turbulence closure , 1975, Journal of Fluid Mechanics.
[3] S. Pope. A more general effective-viscosity hypothesis , 1975, Journal of Fluid Mechanics.
[4] W. Rodi. A new algebraic relation for calculating the Reynolds stresses , 1976 .
[5] Akira Yoshizawa,et al. Turbulent channel and Couette flows using an anisotropic k-epsilon model , 1987 .
[6] T. Gatski,et al. On explicit algebraic stress models for complex turbulent flows , 1992, Journal of Fluid Mechanics.
[7] Kevin W. Bowyer,et al. Combination of multiple classifiers using local accuracy estimates , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[8] Michel Deville,et al. On predicting the turbulence‐induced secondary flows using nonlinear k‐ε models , 1996 .
[9] B. Launder,et al. Development and application of a cubic eddy-viscosity model of turbulence , 1996 .
[10] Christopher M. Bishop,et al. Classification and regression , 1997 .
[11] Thorsten Joachims,et al. Making large scale SVM learning practical , 1998 .
[12] John Kim,et al. DIRECT NUMERICAL SIMULATION OF TURBULENT CHANNEL FLOWS UP TO RE=590 , 1999 .
[13] Sayan Mukherjee,et al. Feature Selection for SVMs , 2000, NIPS.
[14] S. Pope. Turbulent Flows: FUNDAMENTALS , 2000 .
[15] Arne V. Johansson,et al. An explicit algebraic Reynolds stress model for incompressible and compressible turbulent flows , 2000, Journal of Fluid Mechanics.
[16] Nitesh V. Chawla,et al. Creating ensembles of classifiers , 2001, Proceedings 2001 IEEE International Conference on Data Mining.
[17] Nitesh V. Chawla,et al. Distributed Pasting of Small Votes , 2002, Multiple Classifier Systems.
[18] Stefan P. Domino,et al. SIERRA/Fuego: A Multi-Mechanics Fire Environment Simulation Tool , 2003 .
[19] Robert P. Sheridan,et al. Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling , 2003, J. Chem. Inf. Comput. Sci..
[20] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[21] Lawrence O. Hall,et al. A Comparison of Ensemble Creation Techniques , 2004, Multiple Classifier Systems.
[22] Giles Hooker. Diagnosing extrapolation: tree-based density estimation , 2004, KDD '04.
[23] Ivan Bermejo-Moreno,et al. On the non-local geometry of turbulence , 2008, Journal of Fluid Mechanics.
[24] Gianluca Iaccarino,et al. A numerical study of scalar dispersion downstream of a wall-mounted cube using direct simulations and algebraic flux models , 2010 .
[25] Alfredo Pinelli,et al. Reynolds number dependence of mean flow structure in square duct turbulence , 2010, Journal of Fluid Mechanics.
[26] Riccardo Rossi,et al. A numerical study of algebraic flux models for heat and mass transport simulation in complex flows , 2010 .
[27] Qiqi Wang,et al. Uncertainty Quantification of Structural Uncertainties in RANS Simulations of Complex Flows , 2011 .
[28] Gaël Varoquaux,et al. Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..
[29] Todd A. Oliver,et al. Bayesian uncertainty quantification applied to RANS turbulence models , 2011 .
[30] Sai Hung Cheung,et al. Bayesian uncertainty analysis with applications to turbulence modeling , 2011, Reliab. Eng. Syst. Saf..
[31] Qiqi Wang,et al. Quantification of Structural Uncertainties in the k -w Turbulence Model , 2011 .
[32] Gianluca Iaccarino,et al. RANS modeling of turbulent mixing for a jet in supersonic cross flow: model evaluation and uncertainty quantification , 2012 .
[33] Yoav Freund,et al. Boosting: Foundations and Algorithms , 2012 .
[34] Brendan D. Tracey,et al. Application of supervised learning to quantify uncertainties in turbulence and combustion modeling , 2013 .
[35] Srinivasan Arunajatesan,et al. Validation of an FSI Modeling Framework for Internal Captive Carriage Applications. , 2013 .
[36] Gianluca Iaccarino,et al. Numerical analysis and modeling of plume meandering in passive scalar dispersion downstream of a wall-mounted cube , 2013 .
[37] Christopher J. Elkins,et al. Turbulent transport in an inclined jet in crossflow , 2013 .
[38] S. Arunajatesan,et al. Tuning a RANS k-e model for jet-in-crossflow simulations. , 2013 .
[39] Hester Bijl,et al. Bayesian estimates of parameter variability in the k-ε turbulence model , 2014, J. Comput. Phys..
[40] R. D. Radenkovic,et al. Anisotropy analysis of turbulent swirl flow , 2014 .
[41] Srinivasan Arunajatesan,et al. Estimation of k-ε parameters using surrogate models and jet-in-crossflow data , 2014 .
[42] Srinivasan Arunajatesan,et al. Bayesian calibration of a k-e turbulence model for predictive jet-in-crossflow simulations. , 2014 .
[43] Julia Ling,et al. Near Wall Modeling for Trailing Edge Slot Film Cooling , 2015 .
[44] Brendan D. Tracey,et al. A Machine Learning Strategy to Assist Turbulence Model Development , 2015 .
[45] Guilhem Lacaze,et al. Flow topologies and turbulence scales in a jet-in-cross-flow , 2015 .
[46] Anand Pratap Singh,et al. New Approaches in Turbulence and Transition Modeling Using Data-driven Techniques , 2015 .
[47] Julia Ling,et al. Analysis of Turbulent Scalar Flux Models for a Discrete Hole Film Cooling Flow , 2015 .