mlsanityData to 13 Oct 2026

A review on Deep Reinforcement Learning for Fluid Mechanics

arXiv 1908.04127 ↗First posted 12 Aug 2019physics.comp-ph cs.LG physics.flu-dyncited by 65

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Cited by 26 in Deep reinforcement learning methods, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Deep reinforcement learning (DRL) has recently been adopted in a wide range of physics and engineering domains for its ability to solve decision-making problems that were previously out of reach due to a combination of non-linearity and high dimensionality. In the last few years, it has spread in the field of computational mechanics, and particularly in fluid dynamics, with recent applications in flow control and shape optimization. In this work, we conduct a detailed review of existing DRL applications to fluid mechanics problems. In addition, we present recent results that further illustrate the potential of DRL in Fluid Mechanics. The coupling methods used in each case are covered, detailing their advantages and limitations. Our review also focuses on the comparison with classical methods for optimal control and optimization. Finally, several test cases are described that illustrate recent progress made in this field. The goal of this publication is to provide an understanding of DRL capabilities along with state-of-the-art applications in fluid dynamics to researchers wishing to address new problems with these methods.

Key facts. Cited by 65 papers in the map (data through 13 Oct 2026). First posted on arXiv 12 Aug 2019.

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Citing papers per year: 2021: 3; 2022: 11; 2023: 14; 2024: 20; 2025: 10; 2026: 7.

65 papers.

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  1. 2022Machine Learning in Aerodynamic Shape Optimizationcited by 39
  2. 2023Effective control of two-dimensional Rayleigh--B'enard convection: invariant multi-agent reinforcement learning is all you needcited by 25
  3. 2021Physics-informed Dyna-Style Model-Based Deep Reinforcement Learning for Dynamic Controlcited by 22
  4. 2023Deep Learning in Deterministic Computational Mechanicscited by 21
  5. 2022Unsupervised Representation Learning in Deep Reinforcement Learning: A Reviewcited by 18
  6. 2023Recent Advances and Applications of Machine Learning in Experimental Solid Mechanics: A Reviewcited by 15
  7. 2023Physics-aware Machine Learning Revolutionizes Scientific Paradigm for Machine Learning and Process-based Hydrologycited by 11
  8. 2022Multi-fidelity reinforcement learning framework for shape optimizationcited by 9
  9. 2022A Thermodynamics-informed Active Learning Approach to Perception and Reasoning about Fluidscited by 9
  10. 2023Better Neural PDE Solvers Through Data-Free Mesh Moverscited by 9
  11. 2024Multi-agent reinforcement learning for the control of three-dimensional Rayleigh-B'enard convectioncited by 8
  12. 2023Dynamic Feature-based Deep Reinforcement Learning for Flow Control of Circular Cylinder with Sparse Surface Pressure Sensingcited by 7
38 more
  1. 2021Multi-condition multi-objective optimization using deep reinforcement learningcited by 6
  2. 2022Comparative analysis of machine learning methods for active flow controlcited by 6
  3. 2023Turbulence control in plane Couette flow using low-dimensional neural ODE-based models and deep reinforcement learningcited by 5
  4. 2023Learning a model is paramount for sample efficiency in reinforcement learning control of PDEscited by 5
  5. 2023How to Control Hydrodynamic Force on Fluidic Pinball via Deep Reinforcement Learningcited by 5
  6. 2024Advanced deep-reinforcement-learning methods for flow control: group-invariant and positional-encoding networks improve learning speed and qualitycited by 5
  7. 2023A reinforcement learning strategy for p-adaptation in high order solverscited by 4
  8. 2024Model-based deep reinforcement learning for accelerated learning from flow simulationscited by 4
  9. 2024Real-time optimal control of high-dimensional parametrized systems by deep learning-based reduced order modelscited by 4
  10. 2025Transfer learning-enhanced deep reinforcement learning for aerodynamic airfoil optimisation subject to structural constraintscited by 4
  11. 2024Beacon, a lightweight deep reinforcement learning benchmark library for flow controlcited by 3
  12. 2024Enhancing Vehicle Aerodynamics with Deep Reinforcement Learning in Voxelised Modelscited by 3
  13. 2022Investigation of reinforcement learning for shape optimization of profile extrusion diescited by 2
  14. 2023Parallel bootstrap-based on-policy deep reinforcement learning for continuous flow control applicationscited by 2
  15. 2023Effective Latent Differential Equation Models via Attention and Multiple Shootingcited by 2
  16. 2024Physics-Guided Actor-Critic Reinforcement Learning for Swimming in Turbulencecited by 2
  17. 2024A reinforcement learning strategy to automate and accelerate h/p-multigrid solverscited by 2
  18. 2025Control of Rayleigh-B'enard Convection: Effectiveness of Reinforcement Learning in the Turbulent Regimecited by 2
  19. 2025Optimal Lattice Boltzmann Closures through Multi-Agent Reinforcement Learningcited by 2
  20. 2026Plug-and-Play Benchmarking of Reinforcement Learning Algorithms for Large-Scale Flow Controlcited by 2
  21. 2021Evaluating the progress of Deep Reinforcement Learning in the real world: aligning domain-agnostic and domain-specific researchcited by 1
  22. 2022Evolving Pareto-Optimal Actor-Critic Algorithms for Generalizability and Stabilitycited by 1
  23. 2022MeshDQN: A Deep Reinforcement Learning Framework for Improving Meshes in Computational Fluid Dynamicscited by 1
  24. 2024Asynchronous Parallel Reinforcement Learning for Optimizing Propulsive Performance in Fin Ray Controlcited by 1
  25. 2024Real-World Fluid Directed Rigid Body Control via Deep Reinforcement Learningcited by 1
  26. 2024Optimal Parallelization Strategies for Active Flow Control in Deep Reinforcement Learning-Based Computational Fluid Dynamicscited by 1
  27. 2024Policy Optimization for PDE Control with a Warm Startcited by 1
  28. 2024Reinforcement learning for anisotropic p-adaptation and error estimation in high-order solverscited by 1
  29. 2024Deep reinforcement learning for tracking a moving target in jellyfish-like swimmingcited by 1
  30. 2024Multi-modal Policies with Physics-informed Representations in Complex Fluid Environmentscited by 1
  31. 2025A Deep-Learning Iterative Stacked Approach for Prediction of Reactive Dissolution in Porous Mediacited by 1
  32. 2026Reward hacking in physical reinforcement learning revealed by turbulent drag reductioncited by 1
  33. 2026Heuristic Learning for Active Flow Control Using Coding Agentscited by 1
  34. 2022Classifying Turbulent Environments via Machine Learningcited by 0
  35. 2022Data-Driven Evaluation of Training Action Space for Reinforcement Learningcited by 0
  36. 2022Variational multiscale reinforcement learning for discovering reduced order closure models of nonlinear spatiotemporal transport systemscited by 0
  37. 2023An unsupervised machine-learning-based shock sensor for high-order supersonic flow solverscited by 0
  38. 2023Dynamic control of self-assembly of quasicrystalline structures through reinforcement learningcited by 0

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