Related references
Note: Only part of the references are listed.Generalization techniques of neural networks for fluid flow estimation
Masaki Morimoto et al.
NEURAL COMPUTING & APPLICATIONS (2022)
Machine-learning-based spatio-temporal super resolution reconstruction of turbulent flows
Kai Fukami et al.
JOURNAL OF FLUID MECHANICS (2021)
Physics guided machine learning using simplified theories
Suraj Pawar et al.
PHYSICS OF FLUIDS (2021)
PhyGeoNet: Physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state PDEs on irregular domain
Han Gao et al.
JOURNAL OF COMPUTATIONAL PHYSICS (2021)
A point-cloud deep learning framework for prediction of fluid flow fields on irregular geometries
Ali Kashefi et al.
PHYSICS OF FLUIDS (2021)
Convolutional neural network and long short-term memory based reduced order surrogate for minimal turbulent channel flow
Taichi Nakamura et al.
PHYSICS OF FLUIDS (2021)
Reduced-order modeling of advection-dominated systems with recurrent neural networks and convolutional autoencoders
Romit Maulik et al.
PHYSICS OF FLUIDS (2021)
Data-driven modeling of geometry-adaptive steady heat conduction based on convolutional neural networks
Jiang-Zhou Peng et al.
CASE STUDIES IN THERMAL ENGINEERING (2021)
Perspectives on machine learning-augmented Reynolds-averaged and large eddy simulation models of turbulence
Karthik Duraisamy
PHYSICAL REVIEW FLUIDS (2021)
Deep learning of the spanwise-averaged Navier-Stokes equations
Bernat Font et al.
JOURNAL OF COMPUTATIONAL PHYSICS (2021)
Sparse identification of nonlinear dynamics with low-dimensionalized flow representations
Kai Fukami et al.
JOURNAL OF FLUID MECHANICS (2021)
Experimental velocity data estimation for imperfect particle images using machine learning
Masaki Morimoto et al.
PHYSICS OF FLUIDS (2021)
Global field reconstruction from sparse sensors with Voronoi tessellation-assisted deep learning
Kai Fukami et al.
NATURE MACHINE INTELLIGENCE (2021)
Deep Learning Methods for Reynolds-Averaged Navier-Stokes Simulations of Airfoil Flows
Nils Thuerey et al.
AIAA JOURNAL (2020)
Nonlinear mode decomposition with convolutional neural networks for fluid dynamics
Takaaki Murata et al.
JOURNAL OF FLUID MECHANICS (2020)
Prediction of turbulent heat transfer using convolutional neural networks
Junhyuk Kim et al.
JOURNAL OF FLUID MECHANICS (2020)
Deep learning methods for super-resolution reconstruction of turbulent flows
Bo Liu et al.
PHYSICS OF FLUIDS (2020)
A priori analysis on deep learning of subgrid-scale parameterizations for Kraichnan turbulence
Suraj Pawar et al.
THEORETICAL AND COMPUTATIONAL FLUID DYNAMICS (2020)
Assessment of supervised machine learning methods for fluid flows
Kai Fukami et al.
THEORETICAL AND COMPUTATIONAL FLUID DYNAMICS (2020)
Deep unsupervised learning of turbulence for inflow generation at various Reynolds numbers
Junhyuk Kim et al.
JOURNAL OF COMPUTATIONAL PHYSICS (2020)
Machine-learning-based reduced-order modeling for unsteady flows around bluff bodies of various shapes
Kazuto Hasegawa et al.
THEORETICAL AND COMPUTATIONAL FLUID DYNAMICS (2020)
On the surface expression of bottom features in free-surface flow
Saksham Gakhar et al.
JOURNAL OF FLUID MECHANICS (2020)
Shallow neural networks for fluid flow reconstruction with limited sensors
N. Benjamin Erichson et al.
PROCEEDINGS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES (2020)
CNN-LSTM based reduced order modeling of two-dimensional unsteady flows around a circular cylinder at different Reynolds numbers
Kazuto Hasegawa et al.
FLUID DYNAMICS RESEARCH (2020)
Special issue on machine learning and data-driven methods in fluid dynamics
Steven L. Brunton et al.
THEORETICAL AND COMPUTATIONAL FLUID DYNAMICS (2020)
Convolutional neural network based hierarchical autoencoder for nonlinear mode decomposition of fluid field data
Kai Fukami et al.
PHYSICS OF FLUIDS (2020)
Probabilistic neural networks for fluid flow surrogate modeling and data recovery
Romit Maulik et al.
PHYSICAL REVIEW FLUIDS (2020)
Multi-level convolutional autoencoder networks for parametric prediction of spatio-temporal dynamics
Jiayang Xu et al.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING (2020)
Convolutional neural network for transition modeling based on linear stability theory
Muhammad Zafar et al.
PHYSICAL REVIEW FLUIDS (2020)
Turbulence Modeling in the Age of Data
Karthik Duraisamy et al.
ANNUAL REVIEW OF FLUID MECHANICS, VOL 51 (2019)
Dense motion estimation of particle images via a convolutional neural network
Shengze Cai et al.
EXPERIMENTS IN FLUIDS (2019)
Training convolutional neural networks to estimate turbulent sub-grid scale reaction rates
Corentin J. Lapeyre et al.
COMBUSTION AND FLAME (2019)
Super-resolution reconstruction of turbulent flows with machine learning
Kai Fukami et al.
JOURNAL OF FLUID MECHANICS (2019)
Prediction of aerodynamic flow fields using convolutional neural networks
Saakaar Bhatnagar et al.
COMPUTATIONAL MECHANICS (2019)
Online in situ prediction of 3-D flame evolution from its history 2-D projections via deep learning
Jianqing Huang et al.
JOURNAL OF FLUID MECHANICS (2019)
Synthetic turbulent inflow generator using machine learning
Kai Fukami et al.
PHYSICAL REVIEW FLUIDS (2019)
Data-driven prediction of unsteady flow over a circular cylinder using deep learning
Sangseung Lee et al.
JOURNAL OF FLUID MECHANICS (2019)
Recovering missing CFD data for high-order discretizations using deep neural networks and dynamics learning
Kevin T. Carlberg et al.
JOURNAL OF COMPUTATIONAL PHYSICS (2019)
Perspective on machine learning for advancing fluid mechanics
M. P. Brenner et al.
PHYSICAL REVIEW FLUIDS (2019)
Deep learning of mixing by two 'atoms' of stratified turbulence
Hesam Salehipour et al.
JOURNAL OF FLUID MECHANICS (2019)
A novel method of low-dimensional representation for temporal behavior of flow fields using deep autoencoder
Noriyasu Omata et al.
AIP ADVANCES (2019)
Super-resolution reconstruction of turbulent velocity fields using a generative adversarial network-based artificial intelligence framework
Zhiwen Deng et al.
PHYSICS OF FLUIDS (2019)
Deep learning for universal linear embeddings of nonlinear dynamics
Bethany Lusch et al.
NATURE COMMUNICATIONS (2018)
Discriminative Unsupervised Feature Learning with Exemplar Convolutional Neural Networks
Alexey Dosovitskiy et al.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2016)
Network structure of two-dimensional decaying isotropic turbulence
Kunihiko Taira et al.
JOURNAL OF FLUID MECHANICS (2016)
Reynolds averaged turbulence modelling using deep neural networks with embedded invariance
Julia Ling et al.
JOURNAL OF FLUID MECHANICS (2016)
A Few Useful Things to Know About Machine Learning
Pedro Domingos
COMMUNICATIONS OF THE ACM (2012)
A fast immersed boundary method using a nullspace approach and multi-domain far-field boundary conditions
Tim Colonius et al.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING (2008)
The immersed boundary method: A projection approach
Kunihiko Taira et al.
JOURNAL OF COMPUTATIONAL PHYSICS (2007)
Reducing the dimensionality of data with neural networks
G. E. Hinton et al.
SCIENCE (2006)
Neural network modeling for near wall turbulent flow
M Milano et al.
JOURNAL OF COMPUTATIONAL PHYSICS (2002)
Share Paper
