暂无分享,去创建一个
Karthik Duraisamy | Anand Pratap Singh | Shivaji Medida | K. Duraisamy | S. Medida | Anand Pratap Singh
[1] Shigeru Obayashi,et al. Data Assimilation for Turbulent Flows , 2014 .
[2] Jonathan W. Naughton,et al. Skin-friction measurements on the NASA hump model , 2006 .
[3] Todd A. Oliver,et al. Bayesian uncertainty quantification applied to RANS turbulence models , 2011 .
[4] Brendan D. Tracey,et al. A Machine Learning Strategy to Assist Turbulence Model Development , 2015 .
[5] B. Ripley,et al. Pattern Recognition , 1968, Nature.
[6] Gianluca Iaccarino,et al. Modeling of structural uncertainties in Reynolds-averaged Navier-Stokes closures , 2013 .
[7] J. Templeton. Evaluation of machine learning algorithms for prediction of regions of high Reynolds averaged Navier Stokes uncertainty , 2015 .
[8] Karthik Duraisamy,et al. Adjoint-based estimation and control of spatial, temporal and stochastic approximation errors in unsteady flow simulations , 2015 .
[9] Qiqi Wang,et al. Uncertainty Quantification of Structural Uncertainties in RANS Simulations of Complex Flows , 2011 .
[10] Eric G. Paterson,et al. Assessment of DES Models for Separated Flow From a Hump in a Turbulent Boundary Layer , 2009 .
[11] Emilie Sauret,et al. Contribution to single-point closure Reynolds-stress modelling of inhomogeneous flow , 2004 .
[12] Gianluca Iaccarino,et al. RANS modeling of turbulent mixing for a jet in supersonic cross flow: model evaluation and uncertainty quantification , 2012 .
[13] D. Wilcox. Formulation of the k-w Turbulence Model Revisited , 2008 .
[14] J. Templeton,et al. Reynolds averaged turbulence modelling using deep neural networks with embedded invariance , 2016, Journal of Fluid Mechanics.
[15] K. J. Bittorf,et al. VALIDATING THE GALERKIN LEAST-SQUARES FINITE ELEMENT METHODS IN PREDICTING MIXING FLOWS IN STIRRED TANK REACTORS , 2002 .
[16] P. Durbin. Near-wall turbulence closure modeling without “damping functions” , 1991, Theoretical and Computational Fluid Dynamics.
[17] D. M. Somers,et al. Design and experimental results for the S809 airfoil , 1997 .
[18] Sai Hung Cheung,et al. Bayesian uncertainty analysis with applications to turbulence modeling , 2011, Reliab. Eng. Syst. Saf..
[19] P. Spalart. A One-Equation Turbulence Model for Aerodynamic Flows , 1992 .
[20] Christian Igel,et al. Empirical evaluation of the improved Rprop learning algorithms , 2003, Neurocomputing.
[21] Brendan D. Tracey,et al. Application of supervised learning to quantify uncertainties in turbulence and combustion modeling , 2013 .
[22] T. Hughes,et al. A new finite element formulation for computational fluid dynamics. X - The compressible Euler and Navier-Stokes equations , 1991 .
[23] Michele Milano,et al. Neural network modeling for near wall turbulent flow , 2002 .
[24] Ernst Heinrich Hirschel,et al. Comparison of Eddy-Viscosity Turbulence Models in Flows with Adverse Pressure Gradient , 2006 .
[25] P. Durbin. A Reynolds stress model for near-wall turbulence , 1993, Journal of Fluid Mechanics.
[26] Frank Thiele,et al. Restatement of the Spalart-Allmaras Eddy-Viscosity Model in Strain-Adaptive Formulation , 2003 .
[27] Karthik Duraisamy,et al. Efficient Multiscale Gaussian Process Regression using Hierarchical Clustering , 2015, ArXiv.
[28] Gianluca Iaccarino,et al. Modeling Structural Uncertainties in Reynolds-Averaged Computations of Shock/Boundary Layer Interactions , 2011 .
[29] Jack Weatheritt. The development of data driven approaches to further turbulence closures , 2015 .
[30] Niles A. Pierce,et al. An Introduction to the Adjoint Approach to Design , 2000 .
[31] Christopher L. Rumsey,et al. Prediction of high lift: review of present CFD capability , 2002 .
[32] T. B. Gatski,et al. Towards a rational model for the triple velocity correlations of turbulence , 1999, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.
[33] Ted G. Bagwell. CFD Simulation of Flow Tones from Grazing Flow past a Deep Cavity , 2006 .
[34] Victoria Interrante,et al. Real Time Feature Extraction for the Analysis of Turbulent Flows , 2001 .
[35] Karthikeyan Duraisamy,et al. Machine Learning Methods for Data-Driven Turbulence Modeling , 2015 .
[36] Karthik Duraisamy,et al. Computational analysis of vertical axis wind turbine arrays , 2016 .
[37] Anand Pratap Singh,et al. New Approaches in Turbulence and Transition Modeling Using Data-driven Techniques , 2015 .
[38] Sai Hung Cheung,et al. New Bayesian Updating Methodology for Model Validation and Robust Predictions Based on Data from Hierarchical Subsystem Tests , 2008 .
[39] Srinivasan Arunajatesan,et al. Bayesian Calibration of a RANS Model with a Complex Response Surface - A Case Study with Jet-in-Crossflow Configuration , 2015 .
[40] Peter E. Hamlington,et al. Autonomic subgrid-scale closure for large eddy simulations , 2015 .
[41] T. Pulliam,et al. A diagonal form of an implicit approximate-factorization algorithm , 1981 .
[42] Qiqi Wang,et al. Quantification of structural uncertainties in the k − ω turbulence model , 2010 .
[43] D. Todd Griffith,et al. Investigating Aeroelastic Performance of Multi-MegaWatt Wind Turbine Rotors Using CFD , 2012 .
[44] Scott M. Murman,et al. Velocity/Pressure-Gradient Correlations in a FORANS Approach to Turbulence Modeling , 2014 .
[45] James D. Baeder,et al. Analysis of Wind Tunnel Wall Interference Effects on Subsonic Unsteady Airfoil Flows , 2007 .
[46] G. Hulbert,et al. A generalized-α method for integrating the filtered Navier–Stokes equations with a stabilized finite element method , 2000 .
[47] Peter E. Hamlington,et al. A NEW AUTONOMIC CLOSURE FOR LARGE EDDY SIMULATIONS , 2015, Proceeding of Ninth International Symposium on Turbulence and Shear Flow Phenomena.
[48] Heng Xiao,et al. Physics-Informed Machine Learning for Predictive Turbulence Modeling: Using Data to Improve RANS Modeled Reynolds Stresses , 2016 .
[49] Julia Ling,et al. Machine learning strategies for systems with invariance properties , 2016, J. Comput. Phys..
[50] D. M. Somers. Design and experimental results for the S805 airfoil , 1997 .
[51] Hester Bijl,et al. Bayesian estimates of parameter variability in the k-ε turbulence model , 2014, J. Comput. Phys..
[52] B. V. Leer,et al. Towards the ultimate conservative difference scheme V. A second-order sequel to Godunov's method , 1979 .
[53] William Gropp,et al. CFD Vision 2030 Study: A Path to Revolutionary Computational Aerosciences , 2014 .
[54] Heng Xiao,et al. Quantifying and reducing model-form uncertainties in Reynolds-averaged Navier-Stokes simulations: A data-driven, physics-informed Bayesian approach , 2015, J. Comput. Phys..
[55] J. J. Moré,et al. Quasi-Newton Methods, Motivation and Theory , 1974 .
[56] H. Hamann,et al. Estimation of turbulence closure coefficients for data centers using machine learning algorithms , 2012, 13th InterSociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems.
[57] Radford M. Neal. Pattern Recognition and Machine Learning , 2007, Technometrics.
[58] Jinlong Wu,et al. Physics-informed machine learning approach for reconstructing Reynolds stress modeling discrepancies based on DNS data , 2016, 1606.07987.
[59] K. Duraisamy,et al. Using field inversion to quantify functional errors in turbulence closures , 2016 .
[60] P. Spalart,et al. A New Version of Detached-eddy Simulation, Resistant to Ambiguous Grid Densities , 2006 .
[61] D. M. Somers. Design and experimental results for the S814 airfoil , 1997 .
[62] Shivaji Medida,et al. A Comparative Assessment of Correlation-based Transition Models for Wind Power Applications , 2016 .
[63] Steve M. Legensky,et al. A Comparative Aerodynamic Study of Commercial Bicycle Wheels using CFD , 2010 .
[64] T. Hughes,et al. The Galerkin/least-squares method for advective-diffusive equations , 1988 .
[65] Christopher M. Bishop,et al. Current address: Microsoft Research, , 2022 .
[66] Laurent Hascoët,et al. The Tapenade automatic differentiation tool: Principles, model, and specification , 2013, TOMS.
[67] Karthik Duraisamy,et al. A paradigm for data-driven predictive modeling using field inversion and machine learning , 2016, J. Comput. Phys..
[68] P. Roe. Approximate Riemann Solvers, Parameter Vectors, and Difference Schemes , 1997 .
[69] F. Menter. Two-equation eddy-viscosity turbulence models for engineering applications , 1994 .