Inferring turbulent environments via machine learning
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[1] A. Celani,et al. Optimizing airborne wind energy with reinforcement learning , 2022, The European Physical Journal E.
[2] Xiaodong Zhang,et al. Gaussian mixture model for extreme wind turbulence estimation , 2022, Wind Energy Science.
[3] S. Brunton,et al. Enhancing computational fluid dynamics with machine learning , 2021, Nature Computational Science.
[4] Jiaying Liu,et al. Fashion Meets Computer Vision , 2021, ACM Comput. Surv..
[5] Heng Xiao,et al. Frame-independent vector-cloud neural network for nonlocal constitutive modelling on arbitrary grids , 2021, Computer Methods in Applied Mechanics and Engineering.
[6] M. Linkmann,et al. Interpreted machine learning in fluid dynamics: explaining relaminarisation events in wall-bounded shear flows , 2021, Journal of Fluid Mechanics.
[7] S. Griffies,et al. A coarse-grained decomposition of surface geostrophic kinetic energy in the global ocean , 2021 .
[8] X. Wenwei,et al. Deep Learning Experiments for Tropical Cyclone Intensity Forecasts , 2021, Weather and Forecasting.
[9] F. Toschi,et al. Deep learning velocity signals allow quantifying turbulence intensity , 2021, Science Advances.
[10] Stephan Hoyer,et al. Machine learning–accelerated computational fluid dynamics , 2021, Proceedings of the National Academy of Sciences.
[11] M. Buzzicotti,et al. Inertial range statistics of the entropic lattice Boltzmann method in three-dimensional turbulence. , 2021, Physical review. E.
[12] Francesco Borra,et al. Using machine-learning modelling to understand macroscopic dynamics in a system of coupled maps , 2020, ArXiv.
[13] L. Biferale,et al. Reconstruction of turbulent data with deep generative models for semantic inpainting from TURB-Rot database , 2020, Physical Review Fluids.
[14] Alexander Kuhnle,et al. A review on Deep Reinforcement Learning for Fluid Mechanics , 2019, Computers & Fluids.
[15] Gal Chechik,et al. On Learning Sets of Symmetric Elements (Extended Abstract) , 2021, IJCAI.
[16] M. Buzzicotti,et al. Synchronizing subgrid scale models of turbulence to data , 2020, 2012.00690.
[17] Karthik Kashinath,et al. Using Machine Learning to Augment Coarse-Grid Computational Fluid Dynamics Simulations , 2020, ArXiv.
[18] E. Bodenschatz,et al. Self-attenuation of extreme events in Navier–Stokes turbulence , 2020, Nature Communications.
[19] L. Biferale,et al. TURB-Rot. A large database of 3d and 2d snapshots from turbulent rotating flows , 2020, ArXiv.
[20] Wen-Huang Cheng,et al. Fashion Meets Computer Vision , 2020, ACM Comput. Surv..
[21] Luca Biferale,et al. Phase transitions and flux-loop metastable states in rotating turbulence , 2020, Physical Review Fluids.
[22] Ethan Fetaya,et al. On Learning Sets of Symmetric Elements , 2020, ICML.
[23] A. Alexakis,et al. Critical transition in fast-rotating turbulence within highly elongated domains , 2019, Journal of Fluid Mechanics.
[24] Quoc V. Le,et al. EfficientDet: Scalable and Efficient Object Detection , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[25] Petros Koumoutsakos,et al. Machine Learning for Fluid Mechanics , 2019, Annual Review of Fluid Mechanics.
[26] Marc Bocquet,et al. Combining data assimilation and machine learning to emulate a dynamical model from sparse and noisy observations: a case study with the Lorenz 96 model , 2019, J. Comput. Sci..
[27] L. Biferale,et al. Synchronization to Big Data: Nudging the Navier-Stokes Equations for Data Assimilation of Turbulent Flows , 2019, Physical Review X.
[28] Andreas Geiger,et al. Computer Vision for Autonomous Vehicles: Problems, Datasets and State-of-the-Art , 2017, Found. Trends Comput. Graph. Vis..
[29] Uday Pratap Singh,et al. Applications of Computer Vision in Plant Pathology: A Survey , 2019, Archives of Computational Methods in Engineering.
[30] Ming Du,et al. Computer vision algorithms and hardware implementations: A survey , 2019, Integr..
[31] M. P. Brenner,et al. Perspective on machine learning for advancing fluid mechanics , 2019, Physical Review Fluids.
[32] Thomas Peters,et al. Data-driven science and engineering: machine learning, dynamical systems, and control , 2019, Contemporary Physics.
[33] Lakshminarayanan Mahadevan,et al. Controlled gliding and perching through deep-reinforcement-learning , 2019, Physical Review Fluids.
[34] G. Vecchi,et al. Author Correction: Recent increases in tropical cyclone intensification rates , 2019, Nature Communications.
[35] Luca Biferale,et al. Zermelo's problem: Optimal point-to-point navigation in 2D turbulent flows using Reinforcement Learning , 2019, Chaos.
[36] Marc Bocquet,et al. Data assimilation as a learning tool to infer ordinary differential equation representations of dynamical models , 2019, Nonlinear Processes in Geophysics.
[37] Luca Biferale,et al. Self-Similar Subgrid-Scale Models for Inertial Range Turbulence and Accurate Measurements of Intermittency. , 2019, Physical review letters.
[38] Luca Biferale. Rotating turbulence , 2019, Journal of Turbulence.
[39] Steven L. Brunton,et al. Data-Driven Science and Engineering , 2019 .
[40] Charles Meneveau,et al. Application of a self-organizing map to identify the turbulent-boundary-layer interface in a transitional flow , 2019, Physical Review Fluids.
[41] Keith W. Dixon,et al. Recent increases in tropical cyclone intensification rates , 2019, Nature Communications.
[42] Karthik Duraisamy,et al. Turbulence Modeling in the Age of Data , 2018, Annual Review of Fluid Mechanics.
[43] Terrence J. Sejnowski,et al. Glider soaring via reinforcement learning in the field , 2018, Nature.
[44] Luca Biferale,et al. Cascades and transitions in turbulent flows , 2018, Physics Reports.
[45] Prakash Vedula,et al. Subgrid modelling for two-dimensional turbulence using neural networks , 2018, Journal of Fluid Mechanics.
[46] Stanislav Pidhorskyi,et al. Generative Probabilistic Novelty Detection with Adversarial Autoencoders , 2018, NeurIPS.
[47] Luca Biferale,et al. On the inverse energy transfer in rotating turbulence , 2018, The European physical journal. E, Soft matter.
[48] A. Mazzino,et al. Inferring flow parameters and turbulent configuration with physics-informed data assimilation and spectral nudging , 2018, Physical Review Fluids.
[49] Jinlong Wu,et al. Physics-informed machine learning approach for augmenting turbulence models: A comprehensive framework , 2018, Physical Review Fluids.
[50] A. Alexakis,et al. Condensates in rotating turbulent flows , 2017, Journal of Fluid Mechanics.
[51] Luca Biferale,et al. Energy transfer in turbulence under rotation. , 2017, 1711.07054.
[52] Antonio Celani,et al. Flow Navigation by Smart Microswimmers via Reinforcement Learning , 2017, Physical review letters.
[53] I. Mazzitelli,et al. Coherent Structures and Extreme Events in Rotating Multiphase Turbulent Flows , 2016, Physical Review X.
[54] Gautam Reddy,et al. Learning to soar in turbulent environments , 2016, Proceedings of the National Academy of Sciences.
[55] Yuan Yu,et al. TensorFlow: A system for large-scale machine learning , 2016, OSDI.
[56] Alexei A. Efros,et al. Context Encoders: Feature Learning by Inpainting , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[57] Timothy Dozat,et al. Incorporating Nesterov Momentum into Adam , 2016 .
[58] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[59] S. Brunton,et al. Discovering governing equations from data by sparse identification of nonlinear dynamical systems , 2015, Proceedings of the National Academy of Sciences.
[60] Ali Farhadi,et al. You Only Look Once: Unified, Real-Time Object Detection , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[61] Katepalli R Sreenivasan,et al. Extreme events in computational turbulence , 2015, Proceedings of the National Academy of Sciences.
[62] P. Mininni,et al. The spatio-temporal spectrum of turbulent flows , 2015, The European physical journal. E, Soft matter.
[63] J. Templeton. Evaluation of machine learning algorithms for prediction of regions of high Reynolds averaged Navier Stokes uncertainty , 2015 .
[64] P. Mininni,et al. Large-scale anisotropy in stably stratified rotating flows. , 2014, Physical review. E, Statistical, nonlinear, and soft matter physics.
[65] Takemasa Miyoshi,et al. Estimating Model Parameters with Ensemble-Based Data Assimilation: A Review , 2013 .
[66] P. Mininni,et al. Inverse cascades in rotating stratified turbulence: Fast growth of large scales , 2013 .
[67] F. Toschi,et al. Extreme events in the dispersions of two neighboring particles under the influence of fluid turbulence. , 2012, Physical review letters.
[68] B. Efron. A 250-year argument: Belief, behavior, and the bootstrap , 2012 .
[69] Hujun Bao,et al. Laplacian Regularized Gaussian Mixture Model for Data Clustering , 2011, IEEE Transactions on Knowledge and Data Engineering.
[70] P. Mininni,et al. Helicity cascades in rotating turbulence. , 2008, Physical review. E, Statistical, nonlinear, and soft matter physics.
[71] Douglas A. Reynolds,et al. Gaussian Mixture Models , 2018, Encyclopedia of Biometrics.
[72] Michael Ghil,et al. Data assimilation as a nonlinear dynamical systems problem: stability and convergence of the prediction-assimilation system. , 2007, Chaos.
[73] G. Vallis. Atmospheric and Oceanic Fluid Dynamics: Fundamentals and Large-Scale Circulation , 2017 .
[74] Eugenia Kalnay,et al. Atmospheric Modeling, Data Assimilation and Predictability , 2002 .
[75] Ian F. Akyildiz,et al. Wireless sensor networks: a survey , 2002, Comput. Networks.
[76] Carl E. Rasmussen,et al. The Infinite Gaussian Mixture Model , 1999, NIPS.
[77] H. Sebastian Seung,et al. Learning the parts of objects by non-negative matrix factorization , 1999, Nature.
[78] Leslie M. Smith,et al. Transfer of energy to two-dimensional large scales in forced, rotating three-dimensional turbulence , 1999 .
[79] Ionel M. Navon,et al. An Optimal Nudging Data Assimilation Scheme Using Parameter Estimation , 1992 .
[80] David J. C. MacKay,et al. Bayesian Interpolation , 1992, Neural Computation.
[81] J. Holton. Geophysical fluid dynamics. , 1983, Science.