Fetching the paper…
Reading the bibliography…
Neural networks (NNs) and linear stochastic estimation (LSE) have widely been utilized as powerful tools for fluid-flow regressions.
Lumley, J. L. The structure of inhomogeneous turbulent flows. In Yaglom, A. M. & Tatarski, V. I. (eds.) Atmospheric turbulence and radio wave propagation
1967
Earlier work this paper cites.
Rumelhart, D. E., Hinton, G. E. & Williams, R. J. Learning representations by back-propagation errors. Nature
1986
Earlier work this paper cites.
Adrian, R. J. & Moin, P. Stochastic estimation of organized turbulent structure: homogeneous shear flow. J. Fluid Mech
1988
Earlier work this paper cites.
LeCun, Y., Bottou, L., Bengio, Y. & Haffner, P. Gradient-based learning applied to document recognition. Proc. IEEE
1998
Earlier work this paper cites.
Milano, M. & Koumoutsakos, P. Neural network modeling for near wall turbulent flow. J. Comput. Phys
2002
Earlier work this paper cites.
Wang, Z., Bovik, A. C., Sheikh, H. R. & Simoncelli, E. P. Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing
2004
Earlier work this paper cites.
Suzuki, T. & Hasegawa, Y. Estimation of turbulent channel flow at R e τ = 100 Re_{\tau}=100 based on the wall measurement using a simple sequential approach. J. Fluid Mech
2006
Earlier work this paper cites.
Fukagata, K., Kasagi, N. & Koumoutsakos, P. A theoretical prediction of friction drag reduction in turbulent flow by superhydrophobic surfaces. Phys. Fluids
2006
Earlier work this paper cites.
Chevalier, M., Hoepffner, J., Bewley, T. R. & Henningson, D. S. State estimation in wall-bounded flow systems. part 2. turbulent flows. J. Fluid Mech
2006
Earlier work this paper cites.
Nair, V. & Hinton, G. E. Rectified linear units improve restricted boltzmann machines. Proc. Int. Conf. Mach. Learn
2010
Earlier work this paper cites.
Domingos, P. A few useful things to know about machine learning. Commun. ACM
2012
Earlier work this paper cites.
2014
Earlier work this paper cites.
Kor, H., Badri Ghomizad, M. & Fukagata, K. A unified interpolation stencil for ghost-cell immersed boundary method for flow around complex geometries. J. Fluid Sci. Technol
2017
Cited alongside, same era.
Gamahara, M., and Hattori, Y., “Searching for turbulence models by artificial neural network," Phys. Rev. Fluids
2017
Cited alongside, same era.
Loiseau, J.-C., Brunton, S. L. & Noack, B. R. From the POD-Galerkin method to sparse manifold models. doi:10.13140/RG.2.2.27965.31201 (2018)
2018
Cited alongside, same era.
Fukami, K., Fukagata, K. & Taira, K. Super-resolution reconstruction of turbulent flows with machine learning. J. Fluid Mech
2019
Cited alongside, same era.
Brenner, M. P., Eldredge, J. D. & Freund, J. B. Perspective on machine learning for advancing fluid mechanics. Phys. Rev. Fluids
2019
Cited alongside, same era.
Nair, N. J. & Goza, A. Leveraging reduced-order models for state estimation using deep learning. J. Fluid Mech
2020
Later among the works it cites.
Kim, J., and Lee, C., “Prediction of turbulent heat transfer using convolutional neural networks," J. Fluid Mech
2020
Later among the works it cites.
Fukami, K., Fukagata, K. & Taira, K. Machine-learning-based spatio-temporal super resolution reconstruction of turbulent flows. J. Fluid Mech
2021
Closest in time.
Fukami, K., Maulik, K., Ramachandra, N., Fukagata, K. & Taira, K. Global field reconstruction from sparse sensors with Voronoi tessellation-assisted deep learning. Nat. Mach. Intell
2021
Closest in time.
2021
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Duraisamy, K., Iaccarino, G. & Xiao, H. Turbulence modeling in the age of data. Annu. Rev. Fluid. Mech
2019
Cited alongside, same era.
Lee, S. & You, D. Data-driven prediction of unsteady flow fields over a circular cylinder using deep learning. J. Fluid Mech
2019
Cited alongside, same era.
Raissi, M., Perdikaris, P., & Karniadakis, G. E. Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. J. Comput. Phys.,
2019
Cited alongside, same era.
Brunton, S. L., Noack, B. R. & Koumoutsakos, P. Machine learning for fluid mechanics. Annu. Rev. Fluid Mech
2020
Cited alongside, same era.
Fukami, K., Fukagata, K. & Taira, K. Assessment of supervised machine learning for fluid flows. Theor. Comp. Fluid Dyn
2020
Cited alongside, same era.
Maulik, R., Fukami, K., Ramachandra, N., Fukagata, K. & Taira, K. Probabilistic neural networks for fluid flow surrogate modeling and data recovery. Phys. Rev. Fluids
2020
Cited alongside, same era.
Murata, T., Fukami, K. & Fukagata, K. Nonlinear mode decomposition with convolutional neural networks for fluid dynamics. J. Fluid Mech
2020
Cited alongside, same era.
Closest in time.
Morimoto, M., Fukami, K., Zhang, K., Nair, A. G. & Fukagata, K. Convolutional neural networks for fluid flow analysis: toward effective metamodeling and low-dimensionalization. Theor. Comp. Fluid Dyn
2021
Closest in time.
Nakamura, T., Fukami, K., Hasegawa, K., Nabae, Y. & Fukagata, K. Convolutional neural network and long short-term memory based reduced order surrogate for minimal turbulent channel flow. Phys. Fluids
2021
Closest in time.
Park, J., and Choi, H., “Toward neural-network-based large eddy simulation: application to turbulent channel flow," J. Fluid Mech
2021
Closest in time.
2021
Closest in time.
Morimoto, M., Fukami, K., Zhang, K. & Fukagata, K. Generalization techniques of neural networks for fluid flow estimation. Neural Comput. Appl
2021
Closest in time.
Guastoni, L. et al
2021
Closest in time.
Du, Y. & Zaki, T. A. Evolutional deep neural network. Phys. Rev. E
2021
Closest in time.