Deep learning for reduced order modelling and efficient temporal evolution of fluid simulations
Published 2021 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Deep learning for reduced order modelling and efficient temporal evolution of fluid simulations
Authors
Keywords
-
Journal
PHYSICS OF FLUIDS
Volume 33, Issue 10, Pages 107101
Publisher
AIP Publishing
Online
2021-10-01
DOI
10.1063/5.0062546
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Forced convection past a semi-circular cylinder at incidence with a downstream circular cylinder: Thermofluidic transport and stability analysis
- (2021) Sandip Sarkar et al. PHYSICS OF FLUIDS
- Reduced-order modeling of advection-dominated systems with recurrent neural networks and convolutional autoencoders
- (2021) Romit Maulik et al. PHYSICS OF FLUIDS
- A Comprehensive Deep Learning-Based Approach to Reduced Order Modeling of Nonlinear Time-Dependent Parametrized PDEs
- (2021) Stefania Fresca et al. JOURNAL OF SCIENTIFIC COMPUTING
- Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) network
- (2020) Alex Sherstinsky PHYSICA D-NONLINEAR PHENOMENA
- The Adjoint Petrov–Galerkin method for non-linear model reduction
- (2020) Eric J. Parish et al. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
- End-to-end Res-Unet based reconstruction algorithm for photoacoustic imaging
- (2020) Jinchao Feng et al. Biomedical Optics Express
- Single diffusive magnetohydrodynamic pressure driven miscible displacement flows in a channel
- (2019) Sandip Sarkar et al. PHYSICS OF FLUIDS
- Nonlinear mode decomposition with convolutional neural networks for fluid dynamics
- (2019) Takaaki Murata et al. JOURNAL OF FLUID MECHANICS
- Data-driven reduced order model with temporal convolutional neural network
- (2019) Pin Wu et al. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
- Model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders
- (2019) Kookjin Lee et al. JOURNAL OF COMPUTATIONAL PHYSICS
- Neural network closures for nonlinear model order reduction
- (2018) Omer San et al. ADVANCES IN COMPUTATIONAL MATHEMATICS
- Machine learning closures for model order reduction of thermal fluids
- (2018) Omer San et al. APPLIED MATHEMATICAL MODELLING
- Dependence of square cylinder wake on Reynolds number
- (2018) Honglei Bai et al. PHYSICS OF FLUIDS
- Multiobjective Optimal Control Methods for the Navier-Stokes Equations Using Reduced Order Modeling
- (2018) Sebastian Peitz et al. ACTA APPLICANDAE MATHEMATICAE
- Model identification of reduced order fluid dynamics systems using deep learning
- (2017) Z. Wang et al. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN FLUIDS
- Effect of cylinder rotation during mixed convective flow of nanofluids past a circular cylinder
- (2016) Sandip Sarkar et al. COMPUTERS & FLUIDS
- Extracting spatial–temporal coherent patterns in large-scale neural recordings using dynamic mode decomposition
- (2016) Bingni W. Brunton et al. JOURNAL OF NEUROSCIENCE METHODS
- Dynamic Mode Decomposition with Control
- (2016) Joshua L. Proctor et al. SIAM JOURNAL ON APPLIED DYNAMICAL SYSTEMS
- Estimation of perturbations in robotic behavior using dynamic mode decomposition
- (2015) Erik Berger et al. ADVANCED ROBOTICS
- Using functional gains for effective sensor location in flow control: a reduced-order modelling approach
- (2015) Imran Akhtar et al. JOURNAL OF FLUID MECHANICS
- A Web services accessible database of turbulent channel flow and its use for testing a new integral wall model for LES
- (2015) J. Graham et al. JOURNAL OF TURBULENCE
- Mixed convective flow stability of nanofluids past a square cylinder by dynamic mode decomposition
- (2013) Sandip Sarkar et al. INTERNATIONAL JOURNAL OF HEAT AND FLUID FLOW
- Statistically accurate low-order models for uncertainty quantification in turbulent dynamical systems
- (2013) Themistoklis P. Sapsis et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
- A low-cost, goal-oriented ‘compact proper orthogonal decomposition’ basis for model reduction of static systems
- (2010) Kevin Carlberg et al. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING
- Dynamic mode decomposition of numerical and experimental data
- (2010) PETER J. SCHMID JOURNAL OF FLUID MECHANICS
- Flow past a square cylinder with an angle of incidence
- (2010) Dong-Hyeog Yoon et al. PHYSICS OF FLUIDS
- Applications of the dynamic mode decomposition
- (2010) P. J. Schmid et al. THEORETICAL AND COMPUTATIONAL FLUID DYNAMICS
- Numerical methods for low-order modeling of fluid flows based on POD
- (2009) J. Weller et al. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN FLUIDS
- Spectral analysis of nonlinear flows
- (2009) CLARENCE W. ROWLEY et al. JOURNAL OF FLUID MECHANICS
- A new model reduction method for nonlinear dynamical systems
- (2009) Nikolaos Kazantzis et al. NONLINEAR DYNAMICS
- A POD reduced order unstructured mesh ocean modelling method for moderate Reynolds number flows
- (2009) F. Fang et al. OCEAN MODELLING
- Numerical simulation of laminar flow past a circular cylinder
- (2008) B.N. Rajani et al. APPLIED MATHEMATICAL MODELLING
- Steady supersonic flow-field predictions using proper orthogonal decomposition technique
- (2008) Adnan Qamar et al. COMPUTERS & FLUIDS
- A public turbulence database cluster and applications to study Lagrangian evolution of velocity increments in turbulence
- (2008) Yi Li et al. JOURNAL OF TURBULENCE
Create your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create NowBecome a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get Started