Recent progress of machine learning in flow modeling and active flow control

Seamless manga inpainting with semantics awareness

(2021) Minshan Xie et al.   ACM TRANSACTIONS ON GRAPHICS

Deep learning methods for super-resolution reconstruction of turbulent flows

(2020) Bo Liu et al.   PHYSICS OF FLUIDS

Machine learning strategies applied to the control of a fluidic pinball

(2020) C. Raibaudo et al.   PHYSICS OF FLUIDS

Robust active flow control over a range of Reynolds numbers using an artificial neural network trained through deep reinforcement learning

(2020) Hongwei Tang et al.   PHYSICS OF FLUIDS

Flowfield Reconstruction and Shock Train Leading Edge Detection in Scramjet Isolators

(2020) Chen Kong et al.   AIAA JOURNAL

A fault diagnosis model based on weighted extension neural network for turbo-generator sets on small samples with noise

(2020) Tichun WANG et al.   Chinese Journal of Aeronautics

Flow field reconstruction and prediction of the supersonic cascade channel based on a symmetry neural network under complex and variable conditions

(2020) Yunfei Li et al.   AIP Advances

Reliability assessment of engine electronic controllers based on Bayesian deep learning and cloud computing

(2020) Yujia Wang et al.   Chinese Journal of Aeronautics

Adaptive control of transonic buffet flows over an airfoil

(2020) Kai Ren et al.   PHYSICS OF FLUIDS

Online scheduling of image satellites based on neural networks and deep reinforcement learning

(2019) Haijiao WANG et al.   Chinese Journal of Aeronautics

Machine learning methods for turbulence modeling in subsonic flows around airfoils

(2019) Linyang Zhu et al.   PHYSICS OF FLUIDS

A novel method of low-dimensional representation for temporal behavior of flow fields using deep autoencoder

(2019) Noriyasu Omata et al.   AIP Advances

Artificial neural networks trained through deep reinforcement learning discover control strategies for active flow control

(2019) Jean Rabault et al.   JOURNAL OF FLUID MECHANICS

Dense motion estimation of particle images via a convolutional neural network

(2019) Shengze Cai et al.   EXPERIMENTS IN FLUIDS

Quantifying model form uncertainty in Reynolds-averaged turbulence models with Bayesian deep neural networks

(2019) Nicholas Geneva et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Sub-grid scale model classification and blending through deep learning

(2019) Romit Maulik et al.   JOURNAL OF FLUID MECHANICS

Super-resolution reconstruction of turbulent flows with machine learning

(2019) Kai Fukami et al.   JOURNAL OF FLUID MECHANICS

Fast flow field prediction over airfoils using deep learning approach

(2019) Vinothkumar Sekar et al.   PHYSICS OF FLUIDS

Predictions of turbulent shear flows using deep neural networks

(2019) P. A. Srinivasan et al.   Physical Review Fluids

General solutions for nonlinear differential equations: a rule-based self-learning approach using deep reinforcement learning

(2019) Shiyin Wei et al.   COMPUTATIONAL MECHANICS

Prediction of aerodynamic flow fields using convolutional neural networks

(2019) Saakaar Bhatnagar et al.   COMPUTATIONAL MECHANICS

Machine-Learning-Based Detection of Aerodynamic Disturbances Using Surface Pressure Measurements

(2019) Wei Hou et al.   AIAA JOURNAL

Machine Learning for Fluid Mechanics

(2019) Steven L. Brunton et al.   Annual Review of Fluid Mechanics

A new bearing fault diagnosis method based on modified convolutional neural networks

(2019) Jiangquan ZHANG et al.   Chinese Journal of Aeronautics

Rotating machinery fault diagnosis based on convolutional neural network and infrared thermal imaging

(2019) Yongbo LI et al.   Chinese Journal of Aeronautics

Reduced-order Deep Learning for Flow Dynamics. The interplay between deep learning and model reduction

(2019) Siu Wun Cheung et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Data-driven prediction of unsteady flow over a circular cylinder using deep learning

(2019) Sangseung Lee et al.   JOURNAL OF FLUID MECHANICS

Active control of vortex-induced vibration of a circular cylinder using machine learning

(2019) Feng Ren et al.   PHYSICS OF FLUIDS

Reducing the skin-friction drag of a turbulent boundary-layer flow with low-amplitude wall-normal blowing within a Bayesian optimization framework

(2019) O. A. Mahfoze et al.   Physical Review Fluids

Modal Analysis of Fluid Flows: Applications and Outlook

(2019) Kunihiko Taira et al.   AIAA JOURNAL

Prediction of turbulent heat transfer using convolutional neural networks

(2019) Junhyuk Kim et al.   JOURNAL OF FLUID MECHANICS

Inversion and reconstruction of supersonic cascade passage flow field based on a model comprising transposed network and residual network

(2019) Yunfei Li et al.   PHYSICS OF FLUIDS

Perspective on machine learning for advancing fluid mechanics

(2019) M. P. Brenner et al.   Physical Review Fluids

Some Recent Developments in Turbulence Closure Modeling

(2018) Paul A. Durbin   Annual Review of Fluid Mechanics

Hidden physics models: Machine learning of nonlinear partial differential equations

(2018) Maziar Raissi et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Prediction model of velocity field around circular cylinder over various Reynolds numbers by fusion convolutional neural networks based on pressure on the cylinder

(2018) Xiaowei Jin et al.   PHYSICS OF FLUIDS

A reduced-order model for compressible flows with buffeting condition using higher order dynamic mode decomposition with a mode selection criterion

(2018) Jiaqing Kou et al.   PHYSICS OF FLUIDS

Efficient collective swimming by harnessing vortices through deep reinforcement learning

(2018) Siddhartha Verma et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Turbulence Modeling in the Age of Data

(2018) Karthik Duraisamy et al.   Annual Review of Fluid Mechanics

Data mining of a clean signal from highly noisy data based on compressed data fusion: A fast-responding pressure-sensitive paint application

(2018) Xin Wen et al.   PHYSICS OF FLUIDS

Deep learning of vortex-induced vibrations

(2018) Maziar Raissi et al.   JOURNAL OF FLUID MECHANICS

Suppression of fluctuating lift on a cylinder via evolutionary algorithms: Control with interfering small cylinder

(2018) C. Bingham et al.   PHYSICS OF FLUIDS

Investigations of data-driven closure for subgrid-scale stress in large-eddy simulation

(2018) Zhuo Wang et al.   PHYSICS OF FLUIDS

Application of machine learning to viscoplastic flow modeling

(2018) E. Muravleva et al.   PHYSICS OF FLUIDS

Data-driven deconvolution for large eddy simulations of Kraichnan turbulence

(2018) R. Maulik et al.   PHYSICS OF FLUIDS

Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

(2018) M. Raissi et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Drag reduction of a car model by linear genetic programming control

(2017) Ruiying Li et al.   EXPERIMENTS IN FLUIDS

PIV-DCNN: cascaded deep convolutional neural networks for particle image velocimetry

(2017) Yong Lee et al.   EXPERIMENTS IN FLUIDS

Searching for turbulence models by artificial neural network

(2017) Masataka Gamahara et al.   Physical Review Fluids

Linear Closed-Loop Control of Fluid Instabilities and Noise-Induced Perturbations: A Review of Approaches and Tools1

(2016) Denis Sipp et al.   Applied Mechanics Reviews

Closed-loop separation control over a sharp edge ramp using genetic programming

(2016) Antoine Debien et al.   EXPERIMENTS IN FLUIDS

Machine learning strategies for systems with invariance properties

(2016) Julia Ling et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Global effect of local skin friction drag reduction in spatially developing turbulent boundary layer

(2016) A. Stroh et al.   JOURNAL OF FLUID MECHANICS

Reynolds averaged turbulence modelling using deep neural networks with embedded invariance

(2016) Julia Ling et al.   JOURNAL OF FLUID MECHANICS

An investigation of the orthogonal outputs from an on-rotor MEMS accelerometer for reciprocating compressor condition monitoring

(2016) G. Feng et al.   MECHANICAL SYSTEMS AND SIGNAL PROCESSING

A statistical learning strategy for closed-loop control of fluid flows

(2016) Florimond Guéniat et al.   THEORETICAL AND COMPUTATIONAL FLUID DYNAMICS

Closed-Loop Turbulence Control: Progress and Challenges

(2015) Steven L. Brunton et al.   Applied Mechanics Reviews

Closed-loop separation control using machine learning

(2015) N. Gautier et al.   JOURNAL OF FLUID MECHANICS

Human-level control through deep reinforcement learning

(2015) Volodymyr Mnih et al.   NATURE

Evaluation of machine learning algorithms for prediction of regions of high Reynolds averaged Navier Stokes uncertainty

(2015) J. Ling et al.   PHYSICS OF FLUIDS

Transfer Learning for Visual Categorization: A Survey

(2015) Ling Shao et al.   IEEE Transactions on Neural Networks and Learning Systems

Bayesian estimates of parameter variability in the k–ε turbulence model

(2013) W.N. Edeling et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Dynamic mode decomposition of numerical and experimental data

(2010) PETER J. SCHMID   JOURNAL OF FLUID MECHANICS

Assessment of direct numerical simulation data of turbulent boundary layers

(2010) PHILIPP SCHLATTER et al.   JOURNAL OF FLUID MECHANICS

A Survey on Transfer Learning

(2009) Sinno Jialin Pan et al.   IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING

A Method for Handling Uncertainty in Evolutionary Optimization With an Application to Feedback Control of Combustion

(2008) N. Hansen et al.   IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION

Application of compact neural network for drag reduction in a turbulent channel flow at low Reynolds numbers

(2008) Li Vodinh Lorang et al.   PHYSICS OF FLUIDS