暂无分享,去创建一个
[1] Murray Campbell,et al. Deep Blue , 2002, Artif. Intell..
[2] Chao Ma,et al. Modeling subgrid-scale force and divergence of heat flux of compressible isotropic turbulence by artificial neural network , 2019, Physical Review Fluids.
[3] Peter Dayan,et al. Q-learning , 1992, Machine Learning.
[4] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[5] C. Xie,et al. Artificial neural network mixed model for large eddy simulation of compressible isotropic turbulence , 2019, Physics of Fluids.
[6] D. Oberle,et al. Temporal large-eddy simulation based on direct deconvolution , 2020, Physics of Fluids.
[7] Bernhard Schölkopf,et al. A tutorial on support vector regression , 2004, Stat. Comput..
[8] Roger Temam,et al. Approximation of attractors, large eddy simulations and multiscale methods , 1991, Proceedings of the Royal Society of London. Series A: Mathematical and Physical Sciences.
[9] Guigang Zhang,et al. Deep Learning , 2016, Int. J. Semantic Comput..
[10] Koji Fukagata,et al. Synthetic turbulent inflow generator using machine learning , 2018, Physical Review Fluids.
[11] Hua Yang,et al. PIV-DCNN: cascaded deep convolutional neural networks for particle image velocimetry , 2017, Experiments in Fluids.
[12] F. Sarghini,et al. Neural networks based subgrid scale modeling in large eddy simulations , 2003 .
[13] Geoffrey E. Hinton,et al. Autoencoders, Minimum Description Length and Helmholtz Free Energy , 1993, NIPS.
[14] Gopal R. Yalla,et al. Statistical Properties of Subgrid-Scale Turbulence Models , 2021 .
[15] Johan Larsson,et al. Large eddy simulation with modeled wall-stress: recent progress and future directions , 2016 .
[16] Yi Li,et al. A public turbulence database cluster and applications to study Lagrangian evolution of velocity increments in turbulence , 2008, 0804.1703.
[17] Rico Sennrich,et al. Proceedings of SSST-8, Eighth Workshop on Syntax, Semantics and Structure in Statistical Translation , 2014 .
[18] Bernhard Schölkopf,et al. Prior Knowledge in Support Vector Kernels , 1997, NIPS.
[19] Jinlong Wu,et al. Physics-informed machine learning approach for reconstructing Reynolds stress modeling discrepancies based on DNS data , 2016, 1606.07987.
[20] Jürgen Schmidhuber,et al. Long Short-Term Memory , 1997, Neural Computation.
[21] Christophe Eric Corre,et al. Subgrid-scale scalar flux modelling based on optimal estimation theory and machine-learning procedures , 2017 .
[22] Andreas Christmann,et al. Support vector machines , 2008, Data Mining and Knowledge Discovery Handbook.
[23] F ROSENBLATT,et al. The perceptron: a probabilistic model for information storage and organization in the brain. , 1958, Psychological review.
[24] Karthik Duraisamy,et al. A paradigm for data-driven predictive modeling using field inversion and machine learning , 2016, J. Comput. Phys..
[25] Max Welling,et al. Auto-Encoding Variational Bayes , 2013, ICLR.
[26] Anikesh Pal,et al. Deep Learning parameterization of subgrid scales in wall-bounded turbulent flows , 2019, 1905.12765.
[27] Andrea Beck,et al. A machine learning framework for LES closure terms , 2020, ArXiv.
[28] G. He,et al. Subgrid-scale model for large-eddy simulation of isotropic turbulent flows using an artificial neural network , 2019, Computers & Fluids.
[29] Natalia Gimelshein,et al. PyTorch: An Imperative Style, High-Performance Deep Learning Library , 2019, NeurIPS.
[30] J. Nathan Kutz,et al. Deep learning in fluid dynamics , 2017, Journal of Fluid Mechanics.
[31] R. Moser,et al. Optimal LES formulations for isotropic turbulence , 1999, Journal of Fluid Mechanics.
[32] T. Tatsumi. Theory of Homogeneous Turbulence , 1980 .
[33] Been Kim,et al. Towards A Rigorous Science of Interpretable Machine Learning , 2017, 1702.08608.
[34] Quanshi Zhang,et al. Interpretable Convolutional Neural Networks , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[35] 梶島 岳夫. 乱流の数値シミュレーション = Numerical simulation of turbulent flows , 2003 .
[36] Sergei Vassilvitskii,et al. k-means++: the advantages of careful seeding , 2007, SODA '07.
[37] G. Santin,et al. Kernel Methods for Surrogate Modeling , 2019, System- and Data-Driven Methods and Algorithms.
[38] Kurt Hornik,et al. Approximation capabilities of multilayer feedforward networks , 1991, Neural Networks.
[39] W. E,et al. Modeling subgrid-scale forces by spatial artificial neural networks in large eddy simulation of turbulence , 2020 .
[40] C. G. Speziale. Galilean invariance of subgrid-scale stress models in the large-eddy simulation of turbulence , 1985, Journal of Fluid Mechanics.
[41] Omer San,et al. Spatiotemporally dynamic implicit large eddy simulation using machine learning classifiers , 2020 .
[42] Gianluca Geraci,et al. Optimal estimator and artificial neural network as efficient tools for the subgrid-scale scalar flux modeling , 2014 .
[43] A. Mohan,et al. Compressed Convolutional LSTM: An Efficient Deep Learning framework to Model High Fidelity 3D Turbulence , 2019, 1903.00033.
[44] M. Glauser,et al. Whither turbulence and big data in the 21st century , 2017 .
[45] Paola Cinnella,et al. Discovery of Algebraic Reynolds-Stress Models Using Sparse Symbolic Regression , 2019 .
[46] Jan S. Hesthaven,et al. An artificial neural network as a troubled-cell indicator , 2018, J. Comput. Phys..
[47] J. Templeton. Evaluation of machine learning algorithms for prediction of regions of high Reynolds averaged Navier Stokes uncertainty , 2015 .
[48] Andrea D. Beck,et al. A Neural Network based Shock Detection and Localization Approach for Discontinuous Galerkin Methods , 2020, J. Comput. Phys..
[49] Yang Song,et al. Improving the Robustness of Deep Neural Networks via Stability Training , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[50] Karthik Duraisamy,et al. Turbulence Modeling in the Age of Data , 2018, Annual Review of Fluid Mechanics.
[51] 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..
[52] Yuji Hattori,et al. Searching for turbulence models by artificial neural network , 2016, 1607.01042.
[53] Petros Koumoutsakos,et al. Machine Learning for Fluid Mechanics , 2019, Annual Review of Fluid Mechanics.
[54] Liwei Wang,et al. The Expressive Power of Neural Networks: A View from the Width , 2017, NIPS.
[55] Bernhard E. Boser,et al. A training algorithm for optimal margin classifiers , 1992, COLT '92.
[56] Yoshua Bengio,et al. On the Properties of Neural Machine Translation: Encoder–Decoder Approaches , 2014, SSST@EMNLP.
[57] Hava T. Siegelmann,et al. Support Vector Clustering , 2002, J. Mach. Learn. Res..
[58] Yan Wang,et al. Robust active flow control over a range of Reynolds numbers using an artificial neural network trained through deep reinforcement learning. , 2020 .
[59] K. Taira,et al. Super-resolution reconstruction of turbulent flows with machine learning , 2018, Journal of Fluid Mechanics.
[60] Brendan D. Tracey,et al. Application of supervised learning to quantify uncertainties in turbulence and combustion modeling , 2013 .
[61] C. D. Pruett,et al. Toward the De-Mystification of LES , 2001 .
[62] J. Templeton,et al. Reynolds averaged turbulence modelling using deep neural networks with embedded invariance , 2016, Journal of Fluid Mechanics.
[63] Omer San,et al. A neural network approach for the blind deconvolution of turbulent flows , 2017, Journal of Fluid Mechanics.
[64] George Em Karniadakis,et al. NSFnets (Navier-Stokes flow nets): Physics-informed neural networks for the incompressible Navier-Stokes equations , 2020, J. Comput. Phys..
[65] M. Oberlack. Invariant Modelling in Large-Eddy Simulation of Turbulence , 1997 .
[66] A. Kolmogorov. A refinement of previous hypotheses concerning the local structure of turbulence in a viscous incompressible fluid at high Reynolds number , 1962, Journal of Fluid Mechanics.
[67] Jürgen Schmidhuber,et al. Deep learning in neural networks: An overview , 2014, Neural Networks.
[68] Raja Giryes,et al. Autoencoders , 2020, ArXiv.
[69] W S McCulloch,et al. A logical calculus of the ideas immanent in nervous activity , 1990, The Philosophy of Artificial Intelligence.
[70] J. MacQueen. Some methods for classification and analysis of multivariate observations , 1967 .
[71] Katepalli R. Sreenivasan,et al. A perspective on machine learning in turbulent flows , 2020 .
[72] George Cybenko,et al. Approximation by superpositions of a sigmoidal function , 1989, Math. Control. Signals Syst..
[73] George E. Karniadakis,et al. Hidden physics models: Machine learning of nonlinear partial differential equations , 2017, J. Comput. Phys..
[74] Demis Hassabis,et al. Mastering the game of Go with deep neural networks and tree search , 2016, Nature.
[75] John Haugeland,et al. Artificial intelligence - the very idea , 1987 .