- Home
- Publications
- Publication Search
- Publication Details
Title
Multi-fidelity prediction of spatiotemporal fluid flow
Authors
Keywords
-
Journal
PHYSICS OF FLUIDS
Volume 34, Issue 8, Pages 087112
Publisher
AIP Publishing
Online
2022-07-16
DOI
10.1063/5.0099197
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Assessment of unsteady flow predictions using hybrid deep learning based reduced-order models
- (2021) Sandeep Reddy Bukka et al. PHYSICS OF FLUIDS
- A point-cloud deep learning framework for prediction of fluid flow fields on irregular geometries
- (2021) Ali Kashefi 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
- Applying deep reinforcement learning to active flow control in weakly turbulent conditions
- (2021) Feng Ren et al. PHYSICS OF FLUIDS
- Experimental velocity data estimation for imperfect particle images using machine learning
- (2021) Masaki Morimoto et al. PHYSICS OF FLUIDS
- Reduced order model using convolutional auto-encoder with self-attention
- (2021) Pin Wu et al. PHYSICS OF FLUIDS
- From active learning to deep reinforcement learning: Intelligent active flow control in suppressing vortex-induced vibration
- (2021) Changdong Zheng et al. PHYSICS OF FLUIDS
- Deep learning methods for super-resolution reconstruction of turbulent flows
- (2020) Bo Liu et al. PHYSICS OF FLUIDS
- Assessment of supervised machine learning methods for fluid flows
- (2020) Kai Fukami et al. THEORETICAL AND COMPUTATIONAL FLUID DYNAMICS
- Palabos: Parallel Lattice Boltzmann Solver
- (2020) Jonas Latt et al. COMPUTERS & MATHEMATICS WITH APPLICATIONS
- Investigation of Melt Pool Geometry Control in Additive Manufacturing Using Hybrid Modeling
- (2020) Sudeepta Mondal et al. Metals
- Data-Driven Surrogate Modeling ofMultiphase Flows Using Machine Learning Techniques
- (2020) Himakar Ganti et al. COMPUTERS & FLUIDS
- 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
- Special issue on machine learning and data-driven methods in fluid dynamics
- (2020) Steven L. Brunton et al. THEORETICAL AND COMPUTATIONAL FLUID DYNAMICS
- CNN-LSTM based reduced order modeling of two-dimensional unsteady flows around a circular cylinder at different Reynolds numbers
- (2020) Kazuto Hasegawa et al. FLUID DYNAMICS RESEARCH
- Transfer learning based multi-fidelity physics informed deep neural network
- (2020) Souvik Chakraborty JOURNAL OF COMPUTATIONAL PHYSICS
- The von Kármán street behind a circular cylinder: flow control through synthetic jet placed at the rear stagnation point
- (2020) Carlo Salvatore Greco et al. JOURNAL OF FLUID MECHANICS
- Probabilistic neural networks for fluid flow surrogate modeling and data recovery
- (2020) Romit Maulik et al. Physical Review Fluids
- Unsteady reduced-order model of flow over cylinders based on convolutional and deconvolutional neural network structure
- (2020) Jiang-Zhou Peng et al. PHYSICS OF FLUIDS
- Issues in Deciding Whether to Use Multifidelity Surrogates
- (2019) M. Giselle Fernández-Godino et al. AIAA JOURNAL
- Machine Learning for Fluid Mechanics
- (2019) Steven L. Brunton et al. Annual Review of Fluid Mechanics
- Data-driven prediction of unsteady flow over a circular cylinder using deep learning
- (2019) Sangseung Lee et al. JOURNAL OF FLUID MECHANICS
- Multifidelity and Multiscale Bayesian Framework for High-Dimensional Engineering Design and Calibration
- (2019) Soumalya Sarkar et al. JOURNAL OF MECHANICAL DESIGN
- Nonlinear mode decomposition with convolutional neural networks for fluid dynamics
- (2019) Takaaki Murata et al. JOURNAL OF FLUID MECHANICS
- 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
- Correlation Coefficients
- (2018) Patrick Schober et al. ANESTHESIA AND ANALGESIA
- Hidden physics models: Machine learning of nonlinear partial differential equations
- (2018) Maziar Raissi et al. JOURNAL OF COMPUTATIONAL PHYSICS
- Turbulence Modeling in the Age of Data
- (2018) Karthik Duraisamy et al. Annual Review of Fluid Mechanics
- Survey of Multifidelity Methods in Uncertainty Propagation, Inference, and Optimization
- (2018) Benjamin Peherstorfer et al. SIAM REVIEW
- 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
- Nonlinear information fusion algorithms for data-efficient multi-fidelity modelling
- (2017) P. Perdikaris et al. PROCEEDINGS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES
- Multi-fidelity modelling via recursive co-kriging and Gaussian–Markov random fields
- (2015) P. Perdikaris et al. PROCEEDINGS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES
- Artificial viscosity proper orthogonal decomposition
- (2010) Jeff Borggaard et al. MATHEMATICAL AND COMPUTER MODELLING
Add your recorded webinar
Do you already have a recorded webinar? Grow your audience and get more views by easily listing your recording on Peeref.
Upload NowCreate your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create Now