Neural network modeling for near wall turbulent flow

By Michele Milano, Petros Koumoutsakos. 2002

415 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computational Mechanics, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/f0e8198850dae31cc9612940581ec8c059c24475

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In Aerospace Engineering

In Computational Mechanics

In Statistical and Nonlinear Physics

Citations in the corpus

A Physics-Constrained Data-Driven Approach Based on Locally Convex Reconstruction for Noisy Database (2019, background): “Recently, these approaches have been extended to the field of engineering mechanics, such as learning constitutive models in solid mechanics [12–14], surrogate models in fluid mechanics [15–17] and physical models or governing equations purely extracted from the collected data [18– 20].”
Enhancing computational fluid dynamics with machine learning (2021, background): “This may also be accomplished via transfer functions in spectral space [91], convolutional neural networks [92], or modeling the temporal dynamics of the near-wall region via deep neural networks [93].”
Model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders (2018, methodology): “[52], which constructs a nonlinear model of wall turbulence using an autoencoder; Ref.”
Machine learning methods for turbulence modeling in subsonic flows around airfoils (2018, background): “In turbulence, Milano and Koumoutsakos [5] approximated the high order terms by neural networks.”
Machine Learning-augmented Predictive Modeling of Turbulent Separated Flows over Airfoils (2016, methodology): “In the study of turbulent flows, machine-learning techniques appear to have first been used to recreate the behavior of near-wall structures in a turbulent channel flow [7] and to extract coherent spatiotemporal structures [8].”
Nonlinear mode decomposition with convolutional neural networks for fluid dynamics (2019, methodology): “For applications to ﬂuid mechanics, Milano & Koumoutsakos (2002) have successfully demonstrated, through various problems such as the randomly forced Burgers equation and turbulent channel ﬂows, that the capability of an autoencoder is signiﬁcantly improved by adopting nonlinear multi-layer…”
Unsupervised deep learning for super-resolution reconstruction of turbulence (2020, background): “Indeed, there have been attempts to apply NNs to the representation of turbulence (Lee et al. 1997; Milano & Koumoutsakos 2002).”
Constrained sparse Galerkin regression (2016, methodology): “Neural networks have long been used for flow modelling (Milano & Koumoutsakos 2002; Zhang & Duraisamy 2015) and control (Lee et al. 1997), and recently deep neural networks (Krizhevsky, Sutskever & Hinton 2012) have been used for Reynolds averaged turbulence modelling (Ling, Kurzawski & Templeton…”
Quantifying model form uncertainty in Reynolds-averaged turbulence models with Bayesian deep neural networks (2018, methodology): “While the use of machine learning methods in CFD simulations can be traced back to over a decade ago [9], recently there has been a new wave of integrating innovative machine learning algorithms to quantify and improve the accuracy of CFD simulations.”
Data-driven forecasting of high-dimensional chaotic systems with long short-term memory networks (2018, background): “A major beneficiary of this convergence are data-driven dimensionality reduction methods [7, 2, 3, 5, 6, 4, 8], model identification procedures [11, 10, 12, 13, 14, 15, 16, 9] and forecasting techniques [17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31] that aim to provide precise short-te”
Machine learning strategies for systems with invariance properties (2016, neutral): “Milano and Koumoutsakos [9] used the instantaneous velocity”
Data-driven discovery of coordinates and governing equations (2019, neutral): “Nonlinear extensions of PCA have been enabled by a neural network architecture, called an autoencoder (16, 17, 28).”
Data-driven prediction of unsteady flow over a circular cylinder using deep learning (2018, background): “Initial studies of the use of neural networks with sigmoid activation functions to fluid mechanics had been conducted since the late 1990s to the early 2000s [14, 15, 16, 17, 18].”