Machine learning methods for turbulence modeling in subsonic flows around airfoils

Some Recent Developments in Turbulence Closure Modeling

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

Bilinear Convolutional Neural Networks for Fine-Grained Visual Recognition

(2018) Tsung-Yu Lin et al.   IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE

Turbulence dynamics in separated flows: the generalised Kolmogorov equation for inhomogeneous anisotropic conditions

(2018) J.-P. Mollicone et al.   JOURNAL OF FLUID MECHANICS

Quantifying wall turbulence via a symmetry approach. Part 2. Reynolds stresses

(2018) Xi Chen et al.   JOURNAL OF FLUID MECHANICS

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

Turbulence Modeling in the Age of Data

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

Reduced-Order Modeling for Nonlinear Aeroelasticity with Varying Mach Numbers

(2018) Jiaqing Kou et al.   JOURNAL OF AEROSPACE ENGINEERING

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

A hybrid reduced-order framework for complex aeroelastic simulations

(2018) Jiaqing Kou et al.   AEROSPACE SCIENCE AND TECHNOLOGY

A data assimilation model for turbulent flows using continuous adjoint formulation

(2018) Chuangxin He et al.   PHYSICS OF FLUIDS

A Priori Assessment of Prediction Confidence for Data-Driven Turbulence Modeling

(2017) Jin-Long Wu et al.   FLOW TURBULENCE AND COMBUSTION

Layered reduced-order models for nonlinear aerodynamics and aeroelasticity

(2017) Jiaqing Kou et al.   JOURNAL OF FLUIDS AND STRUCTURES

Searching for turbulence models by artificial neural network

(2017) Masataka Gamahara et al.   Physical Review Fluids

Physics-informed machine learning approach for reconstructing Reynolds stress modeling discrepancies based on DNS data

(2017) Jian-Xun Wang et al.   Physical Review Fluids

Nonlinear Aerodynamic Reduced-Order Model for Limit-Cycle Oscillation and Flutter

(2016) Weiwei Zhang et al.   AIAA JOURNAL

A high-order finite volume method on unstructured grids using RBF reconstruction

(2016) Yilang Liu et al.   COMPUTERS & MATHEMATICS WITH APPLICATIONS

Machine learning strategies for systems with invariance properties

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

A paradigm for data-driven predictive modeling using field inversion and machine learning

(2016) Eric J. Parish et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Reynolds averaged turbulence modelling using deep neural networks with embedded invariance

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

Using field inversion to quantify functional errors in turbulence closures

(2016) Anand Pratap Singh et al.   PHYSICS OF FLUIDS

Open/Closed-Loop Aeroservoelastic Predictions via Nonlinear, Reduced-Order Aerodynamic Models

(2015) Rui Huang et al.   AIAA JOURNAL

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

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

Nonlinear Reduced-Order Modeling for Multiple-Input/Multiple-Output Aerodynamic Systems

(2014) Rui Huang et al.   AIAA JOURNAL

Efficient Method for Limit Cycle Flutter Analysis Based on Nonlinear Aerodynamic Reduced-Order Models

(2012) Weiwei Zhang et al.   AIAA JOURNAL

Radial basis function neural network model for mean velocity and vorticity of capillary flow

(2010) Mostafa Y. El-Bakry   INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN FLUIDS

A FULLY COMPLEX-VALUED RADIAL BASIS FUNCTION NETWORK AND ITS LEARNING ALGORITHM

(2009) R. SAVITHA et al.   International Journal of Neural Systems

Data clustering: 50 years beyond K-means

(2009) Anil K. Jain   PATTERN RECOGNITION LETTERS