Prediction of aerodynamic flow fields using convolutional neural networks
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Karthik Duraisamy | Shaowu Pan | Shailendra Kaushik | Yaser Afshar | Saakaar Bhatnagar | K. Duraisamy | S. Kaushik | Shaowu Pan | Saakaar Bhatnagar | Y. Afshar | Yaser Afshar
[1] H. Ng,et al. A Multilayer Convolutional Encoder-Decoder Neural Network for Grammatical Error Correction , 2018, AAAI.
[2] Donghyun You,et al. Prediction of laminar vortex shedding over a cylinder using deep learning , 2017 .
[3] E. Turkel,et al. PRECONDITIONING TECHNIQUES IN COMPUTATIONAL FLUID DYNAMICS , 1999 .
[4] B. V. Leer,et al. Towards the ultimate conservative difference scheme V. A second-order sequel to Godunov's method , 1979 .
[5] Yann LeCun,et al. Deep multi-scale video prediction beyond mean square error , 2015, ICLR.
[6] Karthik Duraisamy,et al. Assessment of Transition Model and CFD Methodology for Wind Turbine Flows , 2012 .
[7] Vladimir G. Kim,et al. Data‐Driven Shape Analysis and Processing , 2015, Comput. Graph. Forum.
[8] Karthikeyan Duraisamy,et al. Studies in Tip Vortex Formation, Evolution and Control , 2005 .
[9] Wang Ping,et al. Research on data augmentation for image classification based on convolution neural networks , 2017, 2017 Chinese Automation Congress (CAC).
[10] Barry Koren,et al. A robust upwind discretization method for advection, diffusion and source terms , 1993 .
[11] P. Roe. CHARACTERISTIC-BASED SCHEMES FOR THE EULER EQUATIONS , 1986 .
[12] Yann LeCun,et al. Convolutional Learning of Spatio-temporal Features , 2010, ECCV.
[13] M. Carter. Computer graphics: Principles and practice , 1997 .
[14] Haibin Ling,et al. Shape Classification Using the Inner-Distance , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[15] George E. Karniadakis,et al. Hidden physics models: Machine learning of nonlinear partial differential equations , 2017, J. Comput. Phys..
[16] George E. Karniadakis,et al. Hidden Fluid Mechanics: A Navier-Stokes Informed Deep Learning Framework for Assimilating Flow Visualization Data , 2018, ArXiv.
[17] James D. Baeder,et al. Application of the Correlation-based Gamma-Re Theta t Transition Model to the Spalart-Allmaras Turbulence Model , 2011 .
[18] Guojun Lu,et al. Review of shape representation and description techniques , 2004, Pattern Recognit..
[19] Hongkai Zhao,et al. A fast sweeping method for Eikonal equations , 2004, Math. Comput..
[20] D. M. Somers. S814 and S815 Airfoils: October 1991--July 1992 , 2004 .
[21] Sajib Saha,et al. Object Class Detection and Classification using Multi Scale Gradient and Corner Point based Shape Descriptors , 2015, ArXiv.
[22] T. P. Miyanawala,et al. An Efficient Deep Learning Technique for the Navier-Stokes Equations: Application to Unsteady Wake Flow Dynamics , 2017, 1710.09099.
[23] V. Leitáo,et al. Computer Graphics: Principles and Practice , 1995 .
[24] Yoshua. Bengio,et al. Learning Deep Architectures for AI , 2007, Found. Trends Mach. Learn..
[25] Hugues Hoppe,et al. Poisson surface reconstruction and its applications , 2008, SPM '08.
[26] Hugues Hoppe. Surface Reconstruction from Unorganized Points (PhD Thesis) , 1994 .
[27] M. Goesele,et al. Floating scale surface reconstruction , 2014, ACM Trans. Graph..
[28] Thomas H. Pulliam,et al. Implementation of Preconditioned Dual-Time Procedures in OVERFLOW , 2003 .
[29] Donghyun You,et al. Data-driven prediction of unsteady flow over a circular cylinder using deep learning , 2018, Journal of Fluid Mechanics.
[30] Robert M. Haralick,et al. Feature normalization and likelihood-based similarity measures for image retrieval , 2001, Pattern Recognit. Lett..
[31] Paris Perdikaris,et al. Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations , 2019, J. Comput. Phys..
[32] Jürgen Schmidhuber,et al. Deep learning in neural networks: An overview , 2014, Neural Networks.
[33] Isaac Amidror,et al. Scattered data interpolation methods for electronic imaging systems: a survey , 2002, J. Electronic Imaging.
[34] Wei Li,et al. Convolutional Neural Networks for Steady Flow Approximation , 2016, KDD.
[35] T. Pulliam,et al. A diagonal form of an implicit approximate-factorization algorithm , 1981 .
[36] Yao Zhang,et al. Application of Convolutional Neural Network to Predict Airfoil Lift Coefficient , 2017, ArXiv.
[37] D. M. Somers,et al. Design and experimental results for the S809 airfoil , 1997 .
[38] P. Spalart. A One-Equation Turbulence Model for Aerodynamic Flows , 1992 .
[39] Richard K. Beatson,et al. Reconstruction and representation of 3D objects with radial basis functions , 2001, SIGGRAPH.
[40] D. M. Somers. Design and experimental results for the S805 airfoil , 1997 .
[41] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[42] Tony DeRose,et al. Surface reconstruction from unorganized points , 1992, SIGGRAPH.
[43] Quoc V. Le,et al. Searching for Activation Functions , 2018, arXiv.
[44] J A Sethian,et al. A fast marching level set method for monotonically advancing fronts. , 1996, Proceedings of the National Academy of Sciences of the United States of America.
[45] James D. Baeder,et al. Computational Investigation of Micro Hovering Rotor Aerodynamics , 2006 .
[46] Karthik Duraisamy,et al. Turbulence Modeling in the Age of Data , 2018, Annual Review of Fluid Mechanics.
[47] Oliver Hennigh,et al. Lat-Net: Compressing Lattice Boltzmann Flow Simulations using Deep Neural Networks , 2017, 1705.09036.
[48] Ken Perlin,et al. Accelerating Eulerian Fluid Simulation With Convolutional Networks , 2016, ICML.
[49] Gang Wang,et al. Convolutional recurrent neural networks: Learning spatial dependencies for image representation , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).