Super-resolution and denoising of fluid flow using physics-informed convolutional neural networks without high-resolution labels

A Bi-fidelity ensemble kalman method for PDE-constrained inverse problems in computational mechanics

(2021) Han Gao et al.   COMPUTATIONAL MECHANICS

Deep learning methods for super-resolution reconstruction of turbulent flows

(2020) Bo Liu et al.   PHYSICS OF FLUIDS

Deep unsupervised learning of turbulence for inflow generation at various Reynolds numbers

(2020) Junhyuk Kim et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Blood flow imaging by optimal matching of computational fluid dynamics to 4D-flow data

(2020) Johannes Töger et al.   MAGNETIC RESONANCE IN MEDICINE

4DFlowNet: Super-Resolution 4D Flow MRI Using Deep Learning and Computational Fluid Dynamics

(2020) Edward Ferdian et al.   Frontiers in Physics

Probabilistic neural networks for fluid flow surrogate modeling and data recovery

(2020) Romit Maulik et al.   Physical Review Fluids

Distributed learning machines for solving forward and inverse problems in partial differential equations

(2020) Vikas Dwivedi et al.   NEUROCOMPUTING

PhyGeoNet: Physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state PDEs on irregular domain

(2020) Han Gao et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Fusing wind-tunnel measurements and CFD data using constrained gappy proper orthogonal decomposition

(2019) M. Mifsud et al.   AEROSPACE SCIENCE AND TECHNOLOGY

Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data

(2019) Yinhao Zhu et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Synthetic turbulent inflow generator using machine learning

(2019) Kai Fukami et al.   Physical Review Fluids

Machine Learning for Fluid Mechanics

(2019) Steven L. Brunton et al.   Annual Review of Fluid Mechanics

Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data

(2019) Luning Sun et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

Modeling the dynamics of PDE systems with physics-constrained deep auto-regressive networks

(2019) Nicholas Geneva et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Perspective on machine learning for advancing fluid mechanics

(2019) M. P. Brenner et al.   Physical Review Fluids

Robust flow reconstruction from limited measurements via sparse representation

(2019) Jared L. Callaham et al.   Physical Review Fluids

PDE-Net 2.0: Learning PDEs from data with a numeric-symbolic hybrid deep network

(2019) Zichao Long et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Data-Driven Sparse Sensor Placement for Reconstruction: Demonstrating the Benefits of Exploiting Known Patterns

(2018) Krithika Manohar et al.   IEEE CONTROL SYSTEMS MAGAZINE

tempoGAN

(2018) You Xie et al.   ACM TRANSACTIONS ON GRAPHICS

Turbulence Modeling in the Age of Data

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

Denoising and Spatial Resolution Enhancement of 4D Flow MRI Using Proper Orthogonal Decomposition and Lasso Regularization

(2018) Mojtaba F. Fathi et al.   COMPUTERIZED MEDICAL IMAGING AND GRAPHICS

Prediction of Reynolds stresses in high-Mach-number turbulent boundary layers using physics-informed machine learning

(2018) Jian-Xun Wang et al.   THEORETICAL AND COMPUTATIONAL FLUID DYNAMICS

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

Data assimilation of mean velocity from 2D PIV measurements of flow over an idealized airfoil

(2017) Sean Symon et al.   EXPERIMENTS IN 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

Aerodynamic database reconstruction via gappy high order singular value decomposition

(2016) Ana I. Moreno et al.   AEROSPACE SCIENCE AND TECHNOLOGY

Data-driven CFD modeling of turbulent flows through complex structures

(2016) Jian-Xun Wang et al.   INTERNATIONAL JOURNAL OF HEAT AND FLUID FLOW

Reconstruction of unsteady viscous flows using data assimilation schemes

(2016) V. Mons et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Quantifying and reducing model-form uncertainties in Reynolds-averaged Navier–Stokes simulations: A data-driven, physics-informed Bayesian approach

(2016) H. Xiao 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

Spatial resolution and velocity field improvement of 4D-flow MRI

(2016) Fraser M. Callaghan et al.   MAGNETIC RESONANCE IN MEDICINE

A particle filter to reconstruct a free-surface flow from a depth camera

(2015) Benoit Combés et al.   FLUID DYNAMICS RESEARCH

Assessment of probability density function based on POD reduced-order model for ensemble-based data assimilation

(2015) Ryota Kikuchi et al.   FLUID DYNAMICS RESEARCH

A data-assimilation method for Reynolds-averaged Navier–Stokes-driven mean flow reconstruction

(2014) Dimitry P. G. Foures et al.   JOURNAL OF FLUID MECHANICS

Robust 4D flow denoising using divergence-free wavelet transform

(2014) Frank Ong et al.   MAGNETIC RESONANCE IN MEDICINE

Dynamic mode decomposition of numerical and experimental data

(2010) PETER J. SCHMID   JOURNAL OF FLUID MECHANICS

Improved SNR in phase contrast velocimetry with five-point balanced flow encoding

(2010) Kevin M. Johnson et al.   MAGNETIC RESONANCE IN MEDICINE

Bayesian inverse problems for functions and applications to fluid mechanics

(2009) S L Cotter et al.   INVERSE PROBLEMS