Machine-learning-based reduced-order modeling for unsteady flows around bluff bodies of various shapes

Assessment of supervised machine learning methods for fluid flows

(2020) Kai Fukami et al.   THEORETICAL AND COMPUTATIONAL FLUID DYNAMICS

Super-resolution reconstruction of turbulent flows with machine learning

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

Predictions of turbulent shear flows using deep neural networks

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

Synthetic turbulent inflow generator using machine learning

(2019) Kai Fukami et al.   Physical Review Fluids

Machine Learning for Fluid Mechanics

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

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

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

Modal Analysis of Fluid Flows: Applications and Outlook

(2019) Kunihiko Taira et al.   AIAA JOURNAL

Nonlinear mode decomposition with convolutional neural networks for fluid dynamics

(2019) Takaaki Murata et al.   JOURNAL OF FLUID MECHANICS

Perspective on machine learning for advancing fluid mechanics

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

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

Numerical simulation and sensitivity analysis of a low-Reynolds-number flow around a square cylinder controlled using plasma actuators

(2017) Yosuke Anzai et al.   Physical Review Fluids

Data-driven approach to design of passive flow control strategies

(2017) F. Gómez et al.   Physical Review Fluids

Reynolds averaged turbulence modelling using deep neural networks with embedded invariance

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

Deep learning

(2015) Yann LeCun et al.   NATURE

Dynamic mode decomposition of numerical and experimental data

(2010) PETER J. SCHMID   JOURNAL OF FLUID MECHANICS