Fast Flow Field Estimation for Various Applications with A Universally Applicable Machine Learning Concept

Fast flow field prediction over airfoils using deep learning approach

(2019) Vinothkumar Sekar et al.   PHYSICS OF FLUIDS

Prediction of aerodynamic flow fields using convolutional neural networks

(2019) Saakaar Bhatnagar et al.   COMPUTATIONAL MECHANICS

Latent Space Physics: Towards Learning the Temporal Evolution of Fluid Flow

(2019) S. Wiewel et al.   COMPUTER GRAPHICS FORUM

Deep Fluids: A Generative Network for Parameterized Fluid Simulations

(2019) Byungsoo Kim et al.   COMPUTER GRAPHICS FORUM

Synthetic turbulent inflow generator using machine learning

(2019) Kai Fukami et al.   Physical Review Fluids

Deep Learning Methods for Reynolds-Averaged Navier–Stokes Simulations of Airfoil Flows

(2019) Nils Thuerey et al.   AIAA JOURNAL

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

(2018) Zhuo Wang et al.   PHYSICS OF FLUIDS

Data-driven deconvolution for large eddy simulations of Kraichnan turbulence

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

Data-driven projection method in fluid simulation

(2016) Cheng Yang et al.   COMPUTER ANIMATION AND VIRTUAL WORLDS

Reynolds averaged turbulence modelling using deep neural networks with embedded invariance

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

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

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

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

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