Key papers

• Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control (2025)
  Andrew Mole et al.
  4.98
• Multi-agent rendezvous in fluid flows via reinforcement learning (2026)
  Bocheng Li et al.
  4.33
• Data-Assimilated Model-Based Reinforcement Learning for Partially Observed Chaotic Flows (2025)
  Defne E. Ozan et al.
  2.76
• Explainable deep reinforcement learning reveals energy-efficient control strategies for turbulent drag reduction (2026)
  Federica Tonti and Ricardo Vinuesa
  1.94
• Self-Evolving Scientific Agent Discovers Generalizable Physically-Reasoned Fluid Control (2026)
  Boai Sun et al.
  1.94
• High-lift Wing Separation Control via Bayesian Optimization and Deep Reinforcement Learning (2026)
  Ricard Montal\`a and Bernat Font and Oriol Lehmkuhl and Ricardo Vinuesa and Ivette Rodriguez
  1.89
• Timescale Separation Enables Deep Reinforcement Learning Control of Rotating Detonation Engine Mode Transitions (2026)
  Kristian Holme et al.
  1.83
• Hypersonic Flow Control: Generalized Deep Reinforcement Learning for Hypersonic Intake Unstart Control under Uncertainty (2026)
  Trishit Mondal and Ameya D. Jagtap
  1.72
• HydroGym: A Reinforcement Learning Platform for Fluid Dynamics (2025)
  Christian Lagemann et al.
  1.61
• Improving the Robustness of Control of Chaotic Convective Flows with Domain-Informed Reinforcement Learning (2025)
  Michiel Straat et al.
  1.56
• Shocks Under Control: Taming Transonic Compressible Flow over an RAE2822 Airfoil with Deep Reinforcement Learning (2025)
  Trishit Mondal et al.
  1.56
• Physics-informed Neural-operator Predictive Control for Drag Reduction in Turbulent Flows (2025)
  Zelin Zhao et al.
  1.50
• Transfer learning strategies for accelerating reinforcement-learning-based flow control (2025)
  Saeed Salehi
  1.50
• Navigation in a Three-Dimensional Urban Flow using Deep Reinforcement Learning (2025)
  Federica Tonti and Ricardo Vinuesa
  1.50
• Deep Reinforcement Learning in Action: Real-Time Control of Vortex-Induced Vibrations (2025)
  Hussam Sababha et al.
  1.44
• LinguaFluid: Language Guided Fluid Control via Semantic Rewards in Reinforcement Learning (2025)
  Aoming Liang et al.
  1.39
• Optimizing Metachronal Paddling with Reinforcement Learning at Low Reynolds Number (2025)
  Alana A. Bailey et al.
  1.33
• Attention on flow control: transformer-based reinforcement learning for lift regulation in highly disturbed flows (2025)
  Zhecheng Liu and Jeff D. Eldredge (University of California et al.
  1.28
• A critical assessment of reinforcement learning methods for microswimmer navigation in complex flows (2025)
  Selim Mecanna et al.
  1.22
• Control of Rayleigh-B\'enard Convection: Effectiveness of Reinforcement Learning in the Turbulent Regime (2025)
  Thorben Markmann et al.
  1.17
• Invariant Control Strategies for Active Flow Control using Graph Neural Networks (2025)
  Marius Kurz et al.
  1.11
• Symmetry reduction for deep reinforcement learning active control of chaotic spatiotemporal dynamics (2021)
  Kevin Zeng et al.
  —
• SRL-Assisted AFM: Generating Planar Unstructured Quadrilateral Meshes with Supervised and Reinforcement Learning-Assisted Advancing Front Method (2023)
  Hua Tong and Kuanren Qian and Eni Halilaj and Yongjie Jessica Zhang
  —
• Active Control of Flow over Rotating Cylinder by Multiple Jets using Deep Reinforcement Learning (2023)
  Kamyar Dobakhti et al.
  —
• Active flow control for three-dimensional cylinders through deep reinforcement learning (2023)
  Pol Su\'arez et al.
  —
• Extreme Event Prediction with Multi-agent Reinforcement Learning-based Parametrization of Atmospheric and Oceanic Turbulence (2023)
  Rambod Mojgani and Daniel Waelchli and Yifei Guan and Petros Koumoutsakos and Pedram Hassanzadeh
  —
• Asynchronous Parallel Reinforcement Learning for Optimizing Propulsive Performance in Fin Ray Control (2024)
  Xin-Yang Liu et al.
  —
• Optimal Parallelization Strategies for Active Flow Control in Deep Reinforcement Learning-Based Computational Fluid Dynamics (2024)
  Wang Jia and Hang Xu
  —
• Model-based deep reinforcement learning for accelerated learning from flow simulations (2024)
  Andre Weiner et al.
  —
• Physics-Guided Actor-Critic Reinforcement Learning for Swimming in Turbulence (2024)
  Christopher Koh et al.
  —
• Advanced deep-reinforcement-learning methods for flow control: group-invariant and positional-encoding networks improve learning speed and quality (2024)
  Joongoo Jeon et al.
  —
• Reinforcement learning for anisotropic p-adaptation and error estimation in high-order solvers (2024)
  David Huergo et al.
  —
• Multi-agent reinforcement learning for the control of three-dimensional Rayleigh-B\'enard convection (2024)
  Joel Vasanth et al.
  —
• Model-Based Reinforcement Learning for Control of Strongly-Disturbed Unsteady Aerodynamic Flows (2024)
  Zhecheng Liu (1) et al.
  —
• Deep reinforcement learning for tracking a moving target in jellyfish-like swimming (2024)
  Yihao Chen and Yue Yang
  —
• Towards Active Flow Control Strategies Through Deep Reinforcement Learning (2024)
  Ricard Montal\`a et al.
  —
• Harvesting energy from turbulent winds with Reinforcement Learning (2024)
  Lorenzo Basile et al.
  —