Fourier neural operator approach to large eddy simulation of three-dimensional turbulence

Attention-enhanced neural network models for turbulence simulation

(2022) Wenhui Peng et al.   PHYSICS OF FLUIDS

Stable a posteriori LES of 2D turbulence using convolutional neural networks: Backscattering analysis and generalization to higher Re via transfer learning

(2022) Yifei Guan et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Toward neural-network-based large eddy simulation: application to turbulent channel flow

(2021) Jonghwan Park et al.   JOURNAL OF FLUID MECHANICS

Deep-learning of parametric partial differential equations from sparse and noisy data

(2021) Hao Xu et al.   PHYSICS OF FLUIDS

Predictive models for flame evolution using machine learning: A priori assessment in turbulent flames without and with mean shear

(2021) Jiahao Ren et al.   PHYSICS OF FLUIDS

Machine learning–accelerated computational fluid dynamics

(2021) Dmitrii Kochkov et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Artificial neural network-based spatial gradient models for large-eddy simulation of turbulence

(2021) Yunpeng Wang et al.   AIP Advances

Dynamic iterative approximate deconvolution models for large-eddy simulation of turbulence

(2021) Zelong Yuan et al.   PHYSICS OF FLUIDS

Physics-Informed Probabilistic Learning of Linear Embeddings of Nonlinear Dynamics with Guaranteed Stability

(2020) Shaowu Pan et al.   SIAM JOURNAL ON APPLIED DYNAMICAL SYSTEMS

From Bypass Transition to Flow Control and Data-Driven Turbulence Modeling: An Input–Output Viewpoint

(2020) Mihailo R. Jovanović   Annual Review of Fluid Mechanics

Predictive large-eddy-simulation wall modeling via physics-informed neural networks

(2019) X. I. A. Yang et al.   Physical Review Fluids

Machine Learning for Fluid Mechanics

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

Effect of flow topology on the kinetic energy flux in compressible isotropic turbulence

(2019) Jianchun Wang et al.   JOURNAL OF FLUID MECHANICS

Subgrid-scale model for large-eddy simulation of isotropic turbulent flows using an artificial neural network

(2019) Zhideng Zhou et al.   COMPUTERS & FLUIDS

Deep neural networks for data-driven LES closure models

(2019) Andrea Beck et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Turbulence Modeling in the Age of Data

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

DGM: A deep learning algorithm for solving partial differential equations

(2018) Justin Sirignano et al.   JOURNAL OF COMPUTATIONAL PHYSICS

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

(2018) Zhuo Wang et al.   PHYSICS OF FLUIDS

Deep learning for universal linear embeddings of nonlinear dynamics

(2018) Bethany Lusch et al.   Nature Communications

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

Scale-adaptive subgrid-scale modelling for large-eddy simulation of turbulent flows

(2017) Changping Yu et al.   PHYSICS OF FLUIDS

Reynolds averaged turbulence modelling using deep neural networks with embedded invariance

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

Study of High–Reynolds Number Isotropic Turbulence by Direct Numerical Simulation

(2008) Takashi Ishihara et al.   Annual Review of Fluid Mechanics