Crossref Citations
This article has been cited by the following publications. This list is generated based on data provided by
Crossref.
San, Omer
Maulik, Romit
and
Ahmed, Mansoor
2019.
An artificial neural network framework for reduced order modeling of transient flows.
Communications in Nonlinear Science and Numerical Simulation,
Vol. 77,
Issue. ,
p.
271.
Peng, Jiang-Zhou
Chen, Siheng
Aubry, Nadine
Chen, Zhihua
and
Wu, Wei-Tao
2020.
Unsteady reduced-order model of flow over cylinders based on convolutional and deconvolutional neural network structure.
Physics of Fluids,
Vol. 32,
Issue. 12,
Taira, Kunihiko
Hemati, Maziar S.
Brunton, Steven L.
Sun, Yiyang
Duraisamy, Karthik
Bagheri, Shervin
Dawson, Scott T. M.
and
Yeh, Chi-An
2020.
Modal Analysis of Fluid Flows: Applications and Outlook.
AIAA Journal,
Vol. 58,
Issue. 3,
p.
998.
Fukami, Kai
Fukagata, Koji
and
Taira, Kunihiko
2020.
Assessment of supervised machine learning methods for fluid flows.
Theoretical and Computational Fluid Dynamics,
Vol. 34,
Issue. 4,
p.
497.
Gao, Han
Wang, Jian-Xun
and
Zahr, Matthew J.
2020.
Non-intrusive model reduction of large-scale, nonlinear dynamical systems using deep learning.
Physica D: Nonlinear Phenomena,
Vol. 412,
Issue. ,
p.
132614.
Peng, Jiang-Zhou
Chen, Siheng
Aubry, Nadine
Chen, Zhi-Hua
and
Wu, Wei-Tao
2020.
Time-variant prediction of flow over an airfoil using deep neural network.
Physics of Fluids,
Vol. 32,
Issue. 12,
Kaneko, Kento
Tsai, Ping-Hsuan
and
Fischer, Paul
2020.
Towards model order reduction for fluid-thermal analysis.
Nuclear Engineering and Design,
Vol. 370,
Issue. ,
p.
110866.
Zhang, Jincheng
and
Zhao, Xiaowei
2020.
A novel dynamic wind farm wake model based on deep learning.
Applied Energy,
Vol. 277,
Issue. ,
p.
115552.
Pawar, Suraj
Ahmed, Shady E.
San, Omer
and
Rasheed, Adil
2020.
An Evolve-Then-Correct Reduced Order Model for Hidden Fluid Dynamics.
Mathematics,
Vol. 8,
Issue. 4,
p.
570.
Halder, R.
Damodaran, M.
and
Khoo, B. C.
2020.
Deep Learning Based Reduced Order Model for Airfoil-Gust and Aeroelastic Interaction.
AIAA Journal,
Vol. 58,
Issue. 10,
p.
4304.
Zucatti, Victor
Lui, Hugo F. S.
Pitz, Diogo B.
and
Wolf, William R.
2020.
Assessment of reduced-order modeling strategies for convective heat transfer.
Numerical Heat Transfer, Part A: Applications,
Vol. 77,
Issue. 7,
p.
702.
Costa Nogueira, Alberto
de Sousa Almeida, João Lucas
Auger, Guillaume
and
Watson, Campbell D.
2020.
High Performance Computing.
Vol. 12321,
Issue. ,
p.
116.
Linot, Alec J.
and
Graham, Michael D.
2020.
Deep learning to discover and predict dynamics on an inertial manifold.
Physical Review E,
Vol. 101,
Issue. 6,
Frank, Michael
Drikakis, Dimitris
and
Charissis, Vassilis
2020.
Machine-Learning Methods for Computational Science and Engineering.
Computation,
Vol. 8,
Issue. 1,
p.
15.
Wang, Xu
Kou, Jiaqing
and
Zhang, Weiwei
2021.
A new dynamic stall prediction framework based on symbiosis of experimental and simulation data.
Physics of Fluids,
Vol. 33,
Issue. 12,
Sieber, Moritz
Paschereit, C. Oliver
and
Oberleithner, Kilian
2021.
Stochastic modelling of a noise-driven global instability in a turbulent swirling jet.
Journal of Fluid Mechanics,
Vol. 916,
Issue. ,
Alsayyari, Fahad
Perkó, Zoltán
Tiberga, Marco
Kloosterman, Jan Leen
and
Lathouwers, Danny
2021.
A fully adaptive nonintrusive reduced-order modelling approach for parametrized time-dependent problems.
Computer Methods in Applied Mechanics and Engineering,
Vol. 373,
Issue. ,
p.
113483.
Zucatti, Victor
Wolf, William
and
Bergmann, Michel
2021.
Calibration of projection-based reduced-order models for unsteady compressible flows.
Journal of Computational Physics,
Vol. 433,
Issue. ,
p.
110196.
Queiroz, L.H.
Santos, F.P.
Oliveira, J.P.
and
Souza, M.B.
2021.
Physics-Informed deep learning to predict flow fields in cyclone separators.
Digital Chemical Engineering,
Vol. 1,
Issue. ,
p.
100002.
Ma, Zhan
and
Pan, Wenxiao
2021.
Data-driven nonintrusive reduced order modeling for dynamical systems with moving boundaries using Gaussian process regression.
Computer Methods in Applied Mechanics and Engineering,
Vol. 373,
Issue. ,
p.
113495.