Three-dimensional deep learning-based reduced order model for unsteady flow dynamics with variable Reynolds number

Assessment of unsteady flow predictions using hybrid deep learning based reduced-order models

(2021) Sandeep Reddy Bukka et al.   PHYSICS OF FLUIDS

Data-driven stability analysis and near-wake jet control for the vortex-induced vibration of a sphere

(2021) Amir Chizfahm et al.   PHYSICS OF FLUIDS

A hybrid partitioned deep learning methodology for moving interface and fluid–structure interaction

(2021) Rachit Gupta et al.   COMPUTERS & FLUIDS

A robust and accurate finite element framework for cavitating flows with moving fluid-structure interfaces

(2021) Suraj R. Kashyap et al.   COMPUTERS & MATHEMATICS WITH APPLICATIONS

A fast and accurate physics-informed neural network reduced order model with shallow masked autoencoder

(2021) Youngkyu Kim et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Deep learning observables in computational fluid dynamics

(2020) Kjetil O. Lye et al.   JOURNAL OF COMPUTATIONAL PHYSICS

On the stability of projection-based model order reduction for convection-dominated laminar and turbulent flows

(2020) Sebastian Grimberg et al.   JOURNAL OF COMPUTATIONAL PHYSICS

3D Deep Learning on Medical Images: A Review

(2020) Satya P. Singh et al.   SENSORS

Decomposition of wake dynamics in fluid–structure interaction via low-dimensional models

(2019) T. P. Miyanawala et al.   JOURNAL OF FLUID MECHANICS

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

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

Prediction of aerodynamic flow fields using convolutional neural networks

(2019) Saakaar Bhatnagar et al.   COMPUTATIONAL MECHANICS

Koopman operator-based model reduction for switched-system control of PDEs

(2019) Sebastian Peitz et al.   AUTOMATICA

Data-driven prediction of unsteady flow over a circular cylinder using deep learning

(2019) Sangseung Lee et al.   JOURNAL OF FLUID MECHANICS

Deep learning for multi-year ENSO forecasts

(2019) Yoo-Geun Ham et al.   NATURE

Nonlinear mode decomposition with convolutional neural networks for fluid dynamics

(2019) Takaaki Murata et al.   JOURNAL OF FLUID MECHANICS

Model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders

(2019) Kookjin Lee et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Machine learning closures for model order reduction of thermal fluids

(2018) Omer San et al.   APPLIED MATHEMATICAL MODELLING

Multitask Autoencoder Model for Recovering Human Poses

(2018) Jun Yu et al.   IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS

Deep Coupling Autoencoder for Fault Diagnosis With Multimodal Sensory Data

(2018) Meng Ma et al.   IEEE Transactions on Industrial Informatics

Prediction model of velocity field around circular cylinder over various Reynolds numbers by fusion convolutional neural networks based on pressure on the cylinder

(2018) Xiaowei Jin et al.   PHYSICS OF FLUIDS

A Review of the Autoencoder and Its Variants: A Comparative Perspective from Target Recognition in Synthetic-Aperture Radar Images

(2018) Ganggang Dong et al.   IEEE Geoscience and Remote Sensing Magazine

A hybrid variational Allen-Cahn/ALE scheme for the coupled analysis of two-phase fluid-structure interaction

(2018) Vaibhav Joshi et al.   INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING

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

PIV-DCNN: cascaded deep convolutional neural networks for particle image velocimetry

(2017) Yong Lee et al.   EXPERIMENTS IN FLUIDS

Low-Dose CT With a Residual Encoder-Decoder Convolutional Neural Network

(2017) Hu Chen et al.   IEEE TRANSACTIONS ON MEDICAL IMAGING

Performing particle image velocimetry using artificial neural networks: a proof-of-concept

(2017) Jean Rabault et al.   MEASUREMENT SCIENCE and TECHNOLOGY

Physics-informed machine learning approach for reconstructing Reynolds stress modeling discrepancies based on DNS data

(2017) Jian-Xun Wang et al.   Physical Review Fluids

Partitioned iterative and dynamic subgrid-scale methods for freely vibrating square-section structures at subcritical Reynolds number

(2016) R.K. Jaiman et al.   COMPUTERS & FLUIDS

A paradigm for data-driven predictive modeling using field inversion and machine learning

(2016) Eric J. Parish et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Self-induced flapping dynamics of a flexible inverted foil in a uniform flow

(2015) P. S. Gurugubelli et al.   JOURNAL OF FLUID MECHANICS

Transient fluid–structure interaction with non-matching spatial and temporal discretizations

(2011) R. Jaiman et al.   COMPUTERS & FLUIDS

Balanced POD for model reduction of linear PDE systems: convergence theory

(2011) John R. Singler   NUMERISCHE MATHEMATIK

Dynamic mode decomposition of numerical and experimental data

(2010) PETER J. SCHMID   JOURNAL OF FLUID MECHANICS

Nonlinear Model Reduction via Discrete Empirical Interpolation

(2010) Saifon Chaturantabut et al.   SIAM JOURNAL ON SCIENTIFIC COMPUTING

A Survey on Transfer Learning

(2009) Sinno Jialin Pan et al.   IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING

Optimizing cubature for efficient integration of subspace deformations

(2008) Steven S. An et al.   ACM TRANSACTIONS ON GRAPHICS

Neural networks as a tool for constructing continuous NDVI time series from AVHRR and MODIS

(2008) M. E. Brown et al.   INTERNATIONAL JOURNAL OF REMOTE SENSING