标题
Applying machine learning to study fluid mechanics
作者
关键词
-
出版物
ACTA MECHANICA SINICA
Volume -, Issue -, Pages -
出版商
Springer Science and Business Media LLC
发表日期
2022-01-04
DOI
10.1007/s10409-021-01143-6
参考文献
相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。- Sparse identification of multiphase turbulence closures for coupled fluid–particle flows
- (2021) S. Beetham et al. JOURNAL OF FLUID MECHANICS
- Physical invariance in neural networks for subgrid-scale scalar flux modeling
- (2021) Hugo Frezat et al. Physical Review Fluids
- Solitary water waves created by variations in bathymetry
- (2021) Manuel Quezada de Luna et al. JOURNAL OF FLUID MECHANICS
- Galerkin force model for transient and post-transient dynamics of the fluidic pinball
- (2021) Nan Deng et al. JOURNAL OF FLUID MECHANICS
- Machine learning–accelerated computational fluid dynamics
- (2021) Dmitrii Kochkov et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
- On closures for reduced order models—A spectrum of first-principle to machine-learned avenues
- (2021) Shady E. Ahmed et al. PHYSICS OF FLUIDS
- Data-driven modeling for unsteady aerodynamics and aeroelasticity
- (2021) Jiaqing Kou et al. PROGRESS IN AEROSPACE SCIENCES
- Promoting global stability in data-driven models of quadratic nonlinear dynamics
- (2021) Alan A. Kaptanoglu et al. Physical Review Fluids
- Editorial: Machine Learning and Physical Review Fluids : An Editorial Perspective
- (2021) Michael P. Brenner et al. Physical Review Fluids
- Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations
- (2020) Maziar Raissi et al. SCIENCE
- Physics-Informed Generative Adversarial Networks for Stochastic Differential Equations
- (2020) Liu Yang et al. SIAM JOURNAL ON SCIENTIFIC COMPUTING
- Active flow control using machine learning: A brief review
- (2020) Feng Ren et al. Journal of Hydrodynamics
- Enhancement of shock-capturing methods via machine learning
- (2020) Ben Stevens et al. THEORETICAL AND COMPUTATIONAL FLUID DYNAMICS
- Robust principal component analysis for modal decomposition of corrupt fluid flows
- (2020) Isabel Scherl et al. Physical Review Fluids
- Artificial intelligence control of a turbulent jet
- (2020) Yu Zhou et al. JOURNAL OF FLUID MECHANICS
- Shallow neural networks for fluid flow reconstruction with limited sensors
- (2020) N. Benjamin Erichson et al. PROCEEDINGS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES
- Data-driven modeling of the chaotic thermal convection in an annular thermosyphon
- (2020) Jean-Christophe Loiseau THEORETICAL AND COMPUTATIONAL FLUID DYNAMICS
- Bayesian optimization with output-weighted optimal sampling
- (2020) Antoine Blanchard et al. JOURNAL OF COMPUTATIONAL PHYSICS
- Reinforcement learning for bluff body active flow control in experiments and simulations
- (2020) Dixia Fan et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
- Formulating turbulence closures using sparse regression with embedded form invariance
- (2020) S. Beetham et al. Physical Review Fluids
- Turbulence closure for high Reynolds number airfoil flows by deep neural networks
- (2020) Linyang Zhu et al. AEROSPACE SCIENCE AND TECHNOLOGY
- Machine learning methods for turbulence modeling in subsonic flows around airfoils
- (2019) Linyang Zhu et al. PHYSICS OF FLUIDS
- Linearly Recurrent Autoencoder Networks for Learning Dynamics
- (2019) Samuel E. Otto et al. SIAM JOURNAL ON APPLIED DYNAMICAL SYSTEMS
- Artificial neural networks trained through deep reinforcement learning discover control strategies for active flow control
- (2019) Jean Rabault et al. JOURNAL OF FLUID MECHANICS
- Super-resolution reconstruction of turbulent flows with machine learning
- (2019) Kai Fukami et al. JOURNAL OF FLUID MECHANICS
- Deep neural network for unsteady aerodynamic and aeroelastic modeling across multiple Mach numbers
- (2019) Kai Li et al. NONLINEAR DYNAMICS
- Sparse identification of truncation errors
- (2019) Stephan Thaler et al. JOURNAL OF COMPUTATIONAL PHYSICS
- Learning data-driven discretizations for partial differential equations
- (2019) Yohai Bar-Sinai et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
- Machine Learning for Fluid Mechanics
- (2019) Steven L. Brunton et al. Annual Review of Fluid Mechanics
- Modal Analysis of Fluid Flows: Applications and Outlook
- (2019) Kunihiko Taira et al. AIAA JOURNAL
- Data-driven discovery of coordinates and governing equations
- (2019) Kathleen Champion et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
- Perspective on machine learning for advancing fluid mechanics
- (2019) M. P. Brenner et al. Physical Review Fluids
- Techniques for interpretable machine learning
- (2019) Mengnan Du et al. COMMUNICATIONS OF THE ACM
- Discovery of Algebraic Reynolds-Stress Models Using Sparse Symbolic Regression
- (2019) Martin Schmelzer et al. FLOW TURBULENCE AND COMBUSTION
- A composite neural network that learns from multi-fidelity data: Application to function approximation and inverse PDE problems
- (2019) Xuhui Meng et al. JOURNAL OF COMPUTATIONAL PHYSICS
- Model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders
- (2019) Kookjin Lee et al. JOURNAL OF COMPUTATIONAL PHYSICS
- Low-order model for successive bifurcations of the fluidic pinball
- (2019) Nan Deng et al. JOURNAL OF FLUID MECHANICS
- Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics
- (2018) Christoph Wehmeyer et al. JOURNAL OF CHEMICAL PHYSICS
- Sparse reduced-order modelling: sensor-based dynamics to full-state estimation
- (2018) Jean-Christophe Loiseau et al. JOURNAL OF FLUID MECHANICS
- Constrained sparse Galerkin regression
- (2018) Jean-Christophe Loiseau et al. JOURNAL OF FLUID MECHANICS
- Model-Free Prediction of Large Spatiotemporally Chaotic Systems from Data: A Reservoir Computing Approach
- (2018) Jaideep Pathak et al. PHYSICAL REVIEW LETTERS
- Efficient collective swimming by harnessing vortices through deep reinforcement learning
- (2018) Siddhartha Verma et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
- Data-driven forecasting of high-dimensional chaotic systems with long short-term memory networks
- (2018) Pantelis R. Vlachas et al. PROCEEDINGS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES
- VAMPnets for deep learning of molecular kinetics
- (2018) Andreas Mardt et al. Nature Communications
- Turbulence Modeling in the Age of Data
- (2018) Karthik Duraisamy et al. Annual Review of Fluid Mechanics
- A hybrid reduced-order framework for complex aeroelastic simulations
- (2018) Jiaqing Kou et al. AEROSPACE SCIENCE AND TECHNOLOGY
- Subgrid modelling for two-dimensional turbulence using neural networks
- (2018) R. Maulik et al. JOURNAL OF FLUID MECHANICS
- 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
- Mastering the game of Go without human knowledge
- (2017) David Silver et al. NATURE
- Physics-informed machine learning approach for reconstructing Reynolds stress modeling discrepancies based on DNS data
- (2017) Jian-Xun Wang 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
- Discovering governing equations from data by sparse identification of nonlinear dynamical systems
- (2016) Steven L. Brunton et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
- On long-term boundedness of Galerkin models
- (2015) Michael Schlegel et al. JOURNAL OF FLUID MECHANICS
- Network-theoretic approach to sparsified discrete vortex dynamics
- (2015) Aditya G. Nair et al. JOURNAL OF FLUID MECHANICS
- Human-level control through deep reinforcement learning
- (2015) Volodymyr Mnih et al. NATURE
- A Survey of Projection-Based Model Reduction Methods for Parametric Dynamical Systems
- (2015) Peter Benner et al. SIAM REVIEW
- Cluster-based reduced-order modelling of a mixing layer
- (2014) Eurika Kaiser et al. JOURNAL OF FLUID MECHANICS
- Dynamic mode decomposition of numerical and experimental data
- (2010) PETER J. SCHMID JOURNAL OF FLUID MECHANICS
- Distilling Free-Form Natural Laws from Experimental Data
- (2009) Michael Schmidt et al. SCIENCE
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