Crossref Citations
This article has been cited by the following publications. This list is generated based on data provided by
Crossref.
Castellanos, R.
Cornejo Maceda, G. Y.
de la Fuente, I.
Noack, B. R.
Ianiro, A.
and
Discetti, S.
2022.
Machine-learning flow control with few sensor feedback and measurement noise.
Physics of Fluids,
Vol. 34,
Issue. 4,
Viquerat, J.
Meliga, P.
Larcher, A.
and
Hachem, E.
2022.
A review on deep reinforcement learning for fluid mechanics: An update.
Physics of Fluids,
Vol. 34,
Issue. 11,
Shinde, Saurabh
and
Bali, Harneet Singh
2022.
Instructions with Complex Control-Flow Entailing Machine Learning.
p.
33.
Varela, Pau
Suárez, Pol
Alcántara-Ávila, Francisco
Miró, Arnau
Rabault, Jean
Font, Bernat
García-Cuevas, Luis Miguel
Lehmkuhl, Oriol
and
Vinuesa, Ricardo
2022.
Deep Reinforcement Learning for Flow Control Exploits Different Physics for Increasing Reynolds Number Regimes.
Actuators,
Vol. 11,
Issue. 12,
p.
359.
Yu, Tao
Wu, Xiaoxiong
Yu, Yang
Li, Ruizhe
and
Zhang, Hao
2023.
Establishment and validation of a relationship model between nozzle experiments and CFD results based on convolutional neural network.
Aerospace Science and Technology,
Vol. 142,
Issue. ,
p.
108694.
Xu, Da
and
Zhang, Mengqi
2023.
Reinforcement-learning-based control of convectively unstable flows.
Journal of Fluid Mechanics,
Vol. 954,
Issue. ,
2023.
How to control hydrodynamic force on fluidic pinball via deep reinforcement learning.
Physics of Fluids,
Vol. 35,
Issue. 4,
Gkimisis, Leonidas
Dias, Bruno
Scoggins, James B.
Magin, Thierry
Mendez, Miguel A.
and
Turchi, Alessandro
2023.
Data-Driven Modeling of Hypersonic Reentry Flow with Heat and Mass Transfer.
AIAA Journal,
Vol. 61,
Issue. 8,
p.
3269.
Vignon, C.
Rabault, J.
and
Vinuesa, R.
2023.
Recent advances in applying deep reinforcement learning for flow control: Perspectives and future directions.
Physics of Fluids,
Vol. 35,
Issue. 3,
Masclans, Núria
Vázquez-Novoa, Fernando
Bernades, Marc
Badia, Rosa M.
and
Jofre, Lluís
2023.
Thermodynamics-informed neural network for recovering supercritical fluid thermophysical information from turbulent velocity data.
International Journal of Thermofluids,
Vol. 20,
Issue. ,
p.
100448.
Sirignano, Justin
and
MacArt, Jonathan F.
2023.
Deep learning closure models for large-eddy simulation of flows around bluff bodies.
Journal of Fluid Mechanics,
Vol. 966,
Issue. ,
Vignon, Colin
Rabault, Jean
Vasanth, Joel
Alcántara-Ávila, Francisco
Mortensen, Mikael
and
Vinuesa, Ricardo
2023.
Effective control of two-dimensional Rayleigh–Bénard convection: Invariant multi-agent reinforcement learning is all you need.
Physics of Fluids,
Vol. 35,
Issue. 6,
Ishize, Takeru
Omichi, Hiroshi
and
Fukagata, Koji
2024.
Flow control by a hybrid use of machine learning and control theory.
International Journal of Numerical Methods for Heat & Fluid Flow,
Vol. 34,
Issue. 8,
p.
3253.
Berger, Sandrine
Arroyo Ramo, Andrea
Guillet, Valentin
Lahire, Thibault
Martin, Brice
Jardin, Thierry
Rachelson, Emmanuel
and
Bauerheim, Michaël
2024.
Reliability assessment of off-policy deep reinforcement learning: A benchmark for aerodynamics.
Data-Centric Engineering,
Vol. 5,
Issue. ,
Ren, Feng
Ding, Zihan
Zhao, Yuanpu
and
Song, Dong
2024.
Active control of wake-induced vibration using deep reinforcement learning.
Physics of Fluids,
Vol. 36,
Issue. 12,
Vasanth, Joel
Rabault, Jean
Alcántara-Ávila, Francisco
Mortensen, Mikael
and
Vinuesa, Ricardo
2024.
Multi-agent Reinforcement Learning for the Control of Three-Dimensional Rayleigh–Bénard Convection.
Flow, Turbulence and Combustion,
Li, Yiqing
Noack, Bernd R.
Wang, Tianyu
Cornejo Maceda, Guy Y.
Pickering, Ethan
Shaqarin, Tamir
and
Tyliszczak, Artur
2024.
Jet mixing enhancement with Bayesian optimization, deep learning and persistent data topology.
Journal of Fluid Mechanics,
Vol. 991,
Issue. ,
Xia, Chengwei
Zhang, Junjie
Kerrigan, Eric C.
and
Rigas, Georgios
2024.
Active flow control for bluff body drag reduction using reinforcement learning with partial measurements.
Journal of Fluid Mechanics,
Vol. 981,
Issue. ,
Liu, Xuemin
and
MacArt, Jonathan F.
2024.
Adjoint-based machine learning for active flow control.
Physical Review Fluids,
Vol. 9,
Issue. 1,
Mohammadikalakoo, Babak
Kotsonis, Marios
and
Doan, (Nguyen) Anh Khoa
2024.
Optimization of Tollmien-Schlichting waves control: comparison between a deep reinforcement learning and particle swarm optimization approach.