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Bibliographic Details
Main Author:	Salehi, Saeed
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Fluid Dynamics
Online Access:	https://arxiv.org/abs/2510.16016
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Attention on flow control: transformer-based reinforcement learning for lift regulation in highly disturbed flows
by: Liu, Zhecheng, et al.
Published: (2025)

Advanced deep-reinforcement-learning methods for flow control: group-invariant and positional-encoding networks improve learning speed and quality
by: Jeon, Joongoo, et al.
Published: (2024)

A critical assessment of reinforcement learning methods for microswimmer navigation in complex flows
by: Mecanna, Selim, et al.
Published: (2025)

Explainable deep reinforcement learning reveals energy-efficient control strategies for turbulent drag reduction
by: Tonti, Federica, et al.
Published: (2026)

Multi-agent reinforcement learning for the control of three-dimensional Rayleigh-Bénard convection
by: Vasanth, Joel, et al.
Published: (2024)

Online learning to accelerate nonlinear PDE solvers: applied to multiphase porous media flow
by: Silva, Vinicius L S, et al.
Published: (2025)

Comparison of CNN-based deep learning architectures for unsteady CFD acceleration on small datasets
by: Khanal, Sangam, et al.
Published: (2025)

Quantum reinforcement learning-based active flow control
by: Zhang, Hongfu, et al.
Published: (2026)

Hybrid machine learning models based on physical patterns to accelerate CFD simulations: a short guide on autoregressive models
by: Sengupta, Arindam, et al.
Published: (2025)

Sequential learning based PINNs to overcome temporal domain complexities in unsteady flow past flapping wings
by: Sundar, Rahul, et al.
Published: (2025)

HFNO: an interpretable data-driven decomposition strategy for turbulent flows
by: Cayuela, Marco, et al.
Published: (2025)

A Physics-informed machine learning model for time-dependent wave runup prediction
by: Naeini, Saeed Saviz, et al.
Published: (2024)

Optimal navigation in two-dimensional flows: control theory and reinforcement learning
by: Parfenyev, Vladimir
Published: (2025)

Strategies for energy-efficient flow control leveraging deep reinforcement learning
by: Jia, Wang, et al.
Published: (2024)

Interpreting and generalizing deep learning in physics-based problems with functional linear models
by: Arzani, Amirhossein, et al.
Published: (2023)

Physics-informed KAN PointNet: Deep learning for simultaneous solutions to inverse problems in incompressible flow on numerous irregular geometries
by: Kashefi, Ali, et al.
Published: (2025)

Machine-learning-based multipoint optimization of fluidic injection parameters for improving nozzle performance
by: Yang, Yunjia, et al.
Published: (2024)

Prospects of federated machine learning in fluid dynamics
by: San, Omer, et al.
Published: (2022)

A domain decomposition-based autoregressive deep learning model for unsteady and nonlinear partial differential equations
by: Nidhan, Sheel, et al.
Published: (2024)

Benchmarking machine learning models for predicting aerofoil performance
by: Summerell, Oliver, et al.
Published: (2025)

Application of machine learning algorithm in temperature field reconstruction
by: He, Qianyu, et al.
Published: (2025)

Assessment of machine learning methods for state-to-state approaches
by: Campoli, Lorenzo, et al.
Published: (2021)

On the locality of local neural operator in learning fluid dynamics
by: Ye, Ximeng, et al.
Published: (2023)

Improving CFD simulations by local machine-learned correction
by: Mitra, Peetak, et al.
Published: (2023)

Information-theoretic machine learning for time-varying mode decomposition of separated aerodynamic flows
by: Fukami, Kai, et al.
Published: (2025)

Bayesian inverse Navier-Stokes problems: joint flow field reconstruction and parameter learning
by: Kontogiannis, Alexandros, et al.
Published: (2024)

Active flow control of a turbulent separation bubble through deep reinforcement learning
by: Font, Bernat, et al.
Published: (2024)

Multi-fidelity aerodynamic data fusion by autoencoder transfer learning
by: Nieto-Centenero, Javier, et al.
Published: (2025)

Physics-informed deep-learning applications to experimental fluid mechanics
by: Eivazi, Hamidreza, et al.
Published: (2022)

Multi-fidelity graph-based neural networks architectures to learn Navier-Stokes solutions on non-parametrized 2D domains
by: Songia, Francesco, et al.
Published: (2026)

Inpainting physics: self-supervised learning for context-driven fluid simulation
by: Weidner, Jonas, et al.
Published: (2026)

Rapid aerodynamic prediction of swept wings via physics-embedded transfer learning
by: Yang, Yunjia, et al.
Published: (2024)

Transported Memory Networks accelerating Computational Fluid Dynamics
by: Schulz, Matthias, et al.
Published: (2025)

Explainable deep learning reveals the physical mechanisms behind the turbulent kinetic energy equation
by: Alcántara-Ávila, Francisco, et al.
Published: (2026)

Vector-based loss functions for turbulent flow field inpainting
by: Baker, Samuel J., et al.
Published: (2025)

Deep reinforcement learning based navigation of a jellyfish-like swimmer in flows with obstacles
by: Chen, Yihao, et al.
Published: (2025)

Method of data forward generation with partial differential equations for machine learning modeling in fluid mechanics
by: Chen, Ruilin
Published: (2025)

oRANS: Online optimisation of RANS machine learning models with embedded DNS data generation
by: Dehtyriov, Daniel, et al.
Published: (2025)

Online model learning with data-assimilated reservoir computers
by: Nóvoa, Andrea, et al.
Published: (2025)

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