Related references
Note: Only part of the references are listed.Machine-learning-based spatio-temporal super resolution reconstruction of turbulent flows
Kai Fukami et al.
JOURNAL OF FLUID MECHANICS (2021)
Turbulent scalar flux in inclined jets in crossflow: counter gradient transport and deep learning modelling
Pedro M. Milani et al.
JOURNAL OF FLUID MECHANICS (2021)
Machine Learning for Fluid Mechanics
Steven L. Brunton et al.
ANNUAL REVIEW OF FLUID MECHANICS, VOL 52 (2020)
Modal Analysis of Fluid Flows: Applications and Outlook
Kunihiko Taira et al.
AIAA JOURNAL (2020)
Techniques for Interpretable Machine Learning
Mengnan Du et al.
COMMUNICATIONS OF THE ACM (2020)
Discovery of Algebraic Reynolds-Stress Models Using Sparse Symbolic Regression
Martin Schmelzer et al.
FLOW TURBULENCE AND COMBUSTION (2020)
Generalization of Machine-Learned Turbulent Heat Flux Models Applied to Film Cooling Flows
Pedro M. Milani et al.
JOURNAL OF TURBOMACHINERY-TRANSACTIONS OF THE ASME (2020)
A priori analysis on deep learning of subgrid-scale parameterizations for Kraichnan turbulence
Suraj Pawar et al.
THEORETICAL AND COMPUTATIONAL FLUID DYNAMICS (2020)
Modeling subgrid-scale forces by spatial artificial neural networks in large eddy simulation of turbulence
Chenyue Xie et al.
PHYSICAL REVIEW FLUIDS (2020)
RANS turbulence model development using CFD-driven machine learning
Yaomin Zhao et al.
JOURNAL OF COMPUTATIONAL PHYSICS (2020)
Turbulence closure modeling with data-driven techniques: physical compatibility and consistency considerations
Salar Taghizadeh et al.
NEW JOURNAL OF PHYSICS (2020)
Formulating turbulence closures using sparse regression with embedded form invariance
S. Beetham et al.
PHYSICAL REVIEW FLUIDS (2020)
Mean-flow data assimilation based on minimal correction of turbulence models: Application to turbulent high Reynolds number backward-facing step
Lucas Franceschini et al.
PHYSICAL REVIEW FLUIDS (2020)
DPM: A deep learning PDE augmentation method with application to large-eddy simulation
Justin Sirignano et al.
JOURNAL OF COMPUTATIONAL PHYSICS (2020)
Deconvolutional artificial neural network models for large eddy simulation of turbulence
Zelong Yuan et al.
PHYSICS OF FLUIDS (2020)
Improving the k-ω-γ-Ar transition model by the field inversion and machine learning framework
Muchen Yang et al.
PHYSICS OF FLUIDS (2020)
Turbulence Modeling in the Age of Data
Karthik Duraisamy et al.
ANNUAL REVIEW OF FLUID MECHANICS, VOL 51 (2019)
Training convolutional neural networks to estimate turbulent sub-grid scale reaction rates
Corentin J. Lapeyre et al.
COMBUSTION AND FLAME (2019)
Reynolds-averaged Navier-Stokes equations with explicit data-driven Reynolds stress closure can be ill-conditioned
Jinlong Wu et al.
JOURNAL OF FLUID MECHANICS (2019)
Super-resolution reconstruction of turbulent flows with machine learning
Kai Fukami et al.
JOURNAL OF FLUID MECHANICS (2019)
Quantification of model uncertainty in RANS simulations: A review
Heng Xiao et al.
PROGRESS IN AEROSPACE SCIENCES (2019)
Sensitivity analysis on chaotic dynamical systems by Finite Difference Non-Intrusive Least Squares Shadowing (FD-NILSS)
Angxiu Ni et al.
JOURNAL OF COMPUTATIONAL PHYSICS (2019)
Modeling subgrid-scale force and divergence of heat flux of compressible isotropic turbulence by artificial neural network
Chenyue Xie et al.
PHYSICAL REVIEW FLUIDS (2019)
Subgrid-scale model for large-eddy simulation of isotropic turbulent flows using an artificial neural network
Zhideng Zhou et al.
COMPUTERS & FLUIDS (2019)
Deep neural networks for data-driven LES closure models
Andrea Beck et al.
JOURNAL OF COMPUTATIONAL PHYSICS (2019)
Machine learning methods for turbulence modeling in subsonic flows around airfoils
Linyang Zhu et al.
PHYSICS OF FLUIDS (2019)
Data-Driven Identification of Parametric Partial Differential Equations
Samuel Rudy et al.
SIAM JOURNAL ON APPLIED DYNAMICAL SYSTEMS (2019)
A Framework for Characterizing Structural Uncertainty in Large-Eddy Simulation Closures
Lluis Jofre et al.
FLOW TURBULENCE AND COMBUSTION (2018)
Precomputed Panel Solver for Aerodynamics Simulation
Haoran Xie et al.
ACM TRANSACTIONS ON GRAPHICS (2018)
Some Recent Developments in Turbulence Closure Modeling
Paul A. Durbin
ANNUAL REVIEW OF FLUID MECHANICS, VOL 50 (2018)
Multiple shooting shadowing for sensitivity analysis of chaotic dynamical systems
Patrick J. Blonigan et al.
JOURNAL OF COMPUTATIONAL PHYSICS (2018)
Efficient collective swimming by harnessing vortices through deep reinforcement learning
Siddhartha Verma et al.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2018)
Physics-informed machine learning approach for augmenting turbulence models: A comprehensive framework
Jin-Long Wu et al.
PHYSICAL REVIEW FLUIDS (2018)
The development of algebraic stress models using a novel evolutionary algorithm
J. Weatheritt et al.
INTERNATIONAL JOURNAL OF HEAT AND FLUID FLOW (2017)
Subgrid-scale scalar flux modelling based on optimal estimation theory and machine-learning procedures
A. Vollant et al.
JOURNAL OF TURBULENCE (2017)
Searching for turbulence models by artificial neural network
Masataka Gamahara et al.
PHYSICAL REVIEW FLUIDS (2017)
Non-Markovian closure models for large eddy simulations using the Mori-Zwanzig formalism
Eric J. Parish et al.
PHYSICAL REVIEW FLUIDS (2017)
Physics-informed machine learning approach for reconstructing Reynolds stress modeling discrepancies based on DNS data
Jian-Xun Wang et al.
PHYSICAL REVIEW FLUIDS (2017)
A neural network approach for the blind deconvolution of turbulent flows
R. Maulik et al.
JOURNAL OF FLUID MECHANICS (2017)
A methodology to evaluate statistical errors in DNS data of plane channel flows
Roney L. Thompson et al.
COMPUTERS & FLUIDS (2016)
Using statistical learning to close two-fluid multiphase flow equations for bubbly flows in vertical channels
Ming Ma et al.
INTERNATIONAL JOURNAL OF MULTIPHASE FLOW (2016)
Machine learning strategies for systems with invariance properties
Julia Ling et al.
JOURNAL OF COMPUTATIONAL PHYSICS (2016)
A paradigm for data-driven predictive modeling using field inversion and machine learning
Eric J. Parish et al.
JOURNAL OF COMPUTATIONAL PHYSICS (2016)
A novel evolutionary algorithm applied to algebraic modifications of the RANS stress-strain relationship
Jack Weatheritt et al.
JOURNAL OF COMPUTATIONAL PHYSICS (2016)
Quantifying and reducing model-form uncertainties in Reynolds-averaged Navier-Stokes simulations: A data-driven, physics-informed Bayesian approach
H. Xiao et al.
JOURNAL OF COMPUTATIONAL PHYSICS (2016)
Reynolds averaged turbulence modelling using deep neural networks with embedded invariance
Julia Ling et al.
JOURNAL OF FLUID MECHANICS (2016)
Using field inversion to quantify functional errors in turbulence closures
Anand Pratap Singh et al.
PHYSICS OF FLUIDS (2016)
Sensitivity of flow evolution on turbulence structure
Aashwin A. Mishra et al.
PHYSICAL REVIEW FLUIDS (2016)
Using statistical learning to close two-fluid multiphase flow equations for a simple bubbly system
Ming Ma et al.
PHYSICS OF FLUIDS (2015)
Philosophies and fallacies in turbulence modeling
Philippe R. Spalart
PROGRESS IN AEROSPACE SCIENCES (2015)
Evaluation of Turbulence Models Using Direct Numerical and Large-Eddy Simulation Data
Hassan Raiesi et al.
JOURNAL OF FLUIDS ENGINEERING-TRANSACTIONS OF THE ASME (2011)
Bayesian uncertainty quantification applied to RANS turbulence models
Todd A. Oliver et al.
13TH EUROPEAN TURBULENCE CONFERENCE (ETC13): STATISTICAL ASPECTS, MODELLING AND SIMULATIONS OF TURBULENCE (2011)
A new approach to LES based on explicit filtering
Joseph Mathew et al.
INTERNATIONAL JOURNAL OF HEAT AND FLUID FLOW (2006)
The use of explicit filters in large eddy simulation
TS Lund
COMPUTERS & MATHEMATICS WITH APPLICATIONS (2003)
Algorithm developments for discrete adjoint methods
MB Giles et al.
AIAA JOURNAL (2003)
Neural networks based subgrid scale modeling in large eddy simulations
F Sarghini et al.
COMPUTERS & FLUIDS (2003)
Neural network modeling for near wall turbulent flow
M Milano et al.
JOURNAL OF COMPUTATIONAL PHYSICS (2002)