Generative adversarial networks to infer velocity components in rotating turbulent flows
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[1] Luca Biferale,et al. Data reconstruction of turbulent flows with Gappy POD, Extended POD and Generative Adversarial Networks , 2022, ArXiv.
[2] C. Meneveau,et al. Reconstructing turbulent velocity and pressure fields from under-resolved noisy particle tracks using physics-informed neural networks , 2022, Experiments in Fluids.
[3] R. Vinuesa,et al. A deep-learning approach for reconstructing 3D turbulent flows from 2D observation data , 2022, Scientific Reports.
[4] S. Griffies,et al. Global energy spectrum of the general oceanic circulation , 2022, Nature Communications.
[5] Kai Fukami,et al. Supervised convolutional network for three-dimensional fluid data reconstruction from sectional flow fields with adaptive super-resolution assistance , 2021, 2103.09020.
[6] Hossein Azizpour,et al. From coarse wall measurements to turbulent velocity fields through deep learning , 2021, The Physics of Fluids.
[7] Hyojin Kim,et al. Unsupervised deep learning for super-resolution reconstruction of turbulence , 2020, Journal of Fluid Mechanics.
[8] S. Discetti,et al. Convolutional-network models to predict wall-bounded turbulence from wall quantities , 2020, Journal of Fluid Mechanics.
[9] L. Biferale,et al. Reconstruction of turbulent data with deep generative models for semantic inpainting from TURB-Rot database , 2020, Physical Review Fluids.
[10] L. Biferale,et al. TURB-Rot. A large database of 3d and 2d snapshots from turbulent rotating flows , 2020, ArXiv.
[11] Kai Fukami,et al. Machine-learning-based spatio-temporal super resolution reconstruction of turbulent flows , 2020, Journal of Fluid Mechanics.
[12] Mingwei Lin,et al. Ocean Observation Technologies: A Review , 2020 .
[13] Sravya Nimmagadda,et al. Turbulence Enrichment using Physics-informed Generative Adversarial Networks , 2020, ArXiv.
[14] Luca Biferale,et al. Phase transitions and flux-loop metastable states in rotating turbulence , 2020, Physical Review Fluids.
[15] Xi-yun Lu,et al. Deep learning methods for super-resolution reconstruction of turbulent flows , 2020, Physics of Fluids.
[16] M. D. Ugarte,et al. Filling missing data and smoothing altered data in satellite imagery with a spatial functional procedure , 2019, Stochastic Environmental Research and Risk Assessment.
[17] Luca Biferale,et al. Cascades and transitions in turbulent flows , 2018, Physics Reports.
[18] Chao Zeng,et al. Missing Data Reconstruction in Remote Sensing Image With a Unified Spatial–Temporal–Spectral Deep Convolutional Neural Network , 2018, IEEE Transactions on Geoscience and Remote Sensing.
[19] Luca Biferale,et al. Energy transfer in turbulence under rotation. , 2017, 1711.07054.
[20] A. Barker,et al. Inertial Wave Turbulence Driven by Elliptical Instability. , 2017, Physical review letters.
[21] Lukasz Kaiser,et al. Attention is All you Need , 2017, NIPS.
[22] Luca Biferale,et al. Lagrangian statistics for Navier–Stokes turbulence under Fourier-mode reduction: fractal and homogeneous decimations , 2016, 1701.00351.
[23] Sebastian Nowozin,et al. f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization , 2016, NIPS.
[24] Alexei A. Efros,et al. Context Encoders: Feature Learning by Inpainting , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[25] Gang Yang,et al. Missing Information Reconstruction of Remote Sensing Data: A Technical Review , 2015, IEEE Geoscience and Remote Sensing Magazine.
[26] Fabien S. Godeferd,et al. Structure and Dynamics of Rotating Turbulence: A Review of Recent Experimental and Numerical Results , 2015 .
[27] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[28] Quoc V. Le,et al. Sequence to Sequence Learning with Neural Networks , 2014, NIPS.
[29] Aaron C. Courville,et al. Generative Adversarial Nets , 2014, NIPS.
[30] Eric Blayo,et al. A Consistent Hybrid Variational-Smoothing Data Assimilation Method: Application to a Simple Shallow-Water Model of the Turbulent Midlatitude Ocean , 2011 .
[31] A. Dabas. Observing the atmospheric wind from space , 2010 .
[32] Neville Smith,et al. GODAE The Global Ocean Data Assimilation Experiment , 2009 .
[33] H. Roman,et al. Wavelet analysis of two-dimensional turbulence in a pure electron plasma , 2009 .
[34] Joseph Katz,et al. Using digital holographic microscopy for simultaneous measurements of 3D near wall velocity and wall shear stress in a turbulent boundary layer , 2008 .
[35] A. Pouquet,et al. Scale interactions and scaling laws in rotating flows at moderate Rossby numbers and large Reynolds numbers , 2008, 0802.3714.
[36] Bernhard Wieneke,et al. Tomographic particle image velocimetry , 2006 .
[37] S. Seager,et al. Atmospheric Circulation of Close-In Extrasolar Giant Planets. I. Global, Barotropic, Adiabatic Simulations , 2006, astro-ph/0607338.
[38] Daniele Venturi,et al. Gappy data and reconstruction procedures for flow past a cylinder , 2004, Journal of Fluid Mechanics.
[39] R. Gurka,et al. XPIV–Multi-plane stereoscopic particle image velocimetry , 2004 .
[40] Vladimir Cardos,et al. Rotational Effects on the Boundary-Layer Flow in Wind Turbines , 2004 .
[41] Darryl D. Holm,et al. Resonant interactions in rotating homogeneous three-dimensional turbulence , 2003, Journal of Fluid Mechanics.
[42] J. Borée,et al. Extended proper orthogonal decomposition: a tool to analyse correlated events in turbulent flows , 2003 .
[43] S. Hochreiter,et al. Long Short-Term Memory , 1997, Neural Computation.
[44] Lawrence Sirovich,et al. Karhunen–Loève procedure for gappy data , 1995 .
[45] Roberto Benzi,et al. A random process for the construction of multiaffine fields , 1993 .
[46] Brian L. Sawford,et al. Reynolds number effects in Lagrangian stochastic models of turbulent dispersion , 1991 .
[47] F. L. Dimet,et al. Variational algorithms for analysis and assimilation of meteorological observations: theoretical aspects , 1986 .
[48] Victor Montagud-Camps. Turbulence , 2019, Turbulent Heating and Anisotropy in the Solar Wind.
[49] Yvonne Jaeger,et al. Turbulence: An Introduction for Scientists and Engineers , 2015 .
[50] R. Gurka,et al. XPIV – Multiplane stereoscopic particle image velocimetry , 2004 .
[51] A. Prasad. Particle image velocimetry , 2000 .
[52] M. Farge. Wavelet Transforms and their Applications to Turbulence , 1992 .