# A review on Deep Reinforcement Learning for Fluid Mechanics

arXiv: https://arxiv.org/abs/1908.04127 · HTML: https://mlsanity.com/paper/1908.04127/

## Key facts

- Cited by 66 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Aug 2019.

## Built on by (most cited first, 30 of 66)

- [Machine Learning in Aerodynamic Shape Optimization](https://mlsanity.com/paper/2202.07141/) (2022)
- [Effective control of two-dimensional Rayleigh--B'enard convection: invariant multi-agent reinforcement learning is all you need](https://mlsanity.com/paper/2304.02370/) (2023)
- [Physics-informed Dyna-Style Model-Based Deep Reinforcement Learning for Dynamic Control](https://mlsanity.com/paper/2108.00128/) (2021)
- [Deep Learning in Deterministic Computational Mechanics](https://mlsanity.com/paper/2309.15421/) (2023)
- [Unsupervised Representation Learning in Deep Reinforcement Learning: A Review](https://mlsanity.com/paper/2208.14226/) (2022)
- [Recent Advances and Applications of Machine Learning in Experimental Solid Mechanics: A Review](https://mlsanity.com/paper/2303.07647/) (2023)
- [Physics-aware Machine Learning Revolutionizes Scientific Paradigm for Machine Learning and Process-based Hydrology](https://mlsanity.com/paper/2310.05227/) (2023)
- [Multi-fidelity reinforcement learning framework for shape optimization](https://mlsanity.com/paper/2202.11170/) (2022)
- [A Thermodynamics-informed Active Learning Approach to Perception and Reasoning about Fluids](https://mlsanity.com/paper/2203.05775/) (2022)
- [Better Neural PDE Solvers Through Data-Free Mesh Movers](https://mlsanity.com/paper/2312.05583/) (2023)
- [Multi-agent reinforcement learning for the control of three-dimensional Rayleigh-B'enard convection](https://mlsanity.com/paper/2407.21565/) (2024)
- [Dynamic Feature-based Deep Reinforcement Learning for Flow Control of Circular Cylinder with Sparse Surface Pressure Sensing](https://mlsanity.com/paper/2307.01995/) (2023)
- [Multi-condition multi-objective optimization using deep reinforcement learning](https://mlsanity.com/paper/2110.05945/) (2021)
- [Comparative analysis of machine learning methods for active flow control](https://mlsanity.com/paper/2202.11664/) (2022)
- [Learning a model is paramount for sample efficiency in reinforcement learning control of PDEs](https://mlsanity.com/paper/2302.07160/) (2023)
- [Turbulence control in plane Couette flow using low-dimensional neural ODE-based models and deep reinforcement learning](https://mlsanity.com/paper/2301.12098/) (2023)
- [How to Control Hydrodynamic Force on Fluidic Pinball via Deep Reinforcement Learning](https://mlsanity.com/paper/2304.11526/) (2023)
- [Advanced deep-reinforcement-learning methods for flow control: group-invariant and positional-encoding networks improve learning speed and quality](https://mlsanity.com/paper/2407.17822/) (2024)
- [A reinforcement learning strategy for p-adaptation in high order solvers](https://mlsanity.com/paper/2306.08292/) (2023)
- [Model-based deep reinforcement learning for accelerated learning from flow simulations](https://mlsanity.com/paper/2402.16543/) (2024)
- [Real-time optimal control of high-dimensional parametrized systems by deep learning-based reduced order models](https://mlsanity.com/paper/2409.05709/) (2024)
- [Transfer learning-enhanced deep reinforcement learning for aerodynamic airfoil optimisation subject to structural constraints](https://mlsanity.com/paper/2505.02634/) (2025)
- [Beacon, a lightweight deep reinforcement learning benchmark library for flow control](https://mlsanity.com/paper/2402.17402/) (2024)
- [Enhancing Vehicle Aerodynamics with Deep Reinforcement Learning in Voxelised Models](https://mlsanity.com/paper/2405.11492/) (2024)
- [Investigation of reinforcement learning for shape optimization of profile extrusion dies](https://mlsanity.com/paper/2212.12207/) (2022)
- [Parallel bootstrap-based on-policy deep reinforcement learning for continuous flow control applications](https://mlsanity.com/paper/2304.12330/) (2023)
- [Effective Latent Differential Equation Models via Attention and Multiple Shooting](https://mlsanity.com/paper/2307.05735/) (2023)
- [Physics-Guided Actor-Critic Reinforcement Learning for Swimming in Turbulence](https://mlsanity.com/paper/2406.10242/) (2024)
- [A reinforcement learning strategy to automate and accelerate h/p-multigrid solvers](https://mlsanity.com/paper/2407.15872/) (2024)
- [Control of Rayleigh-B'enard Convection: Effectiveness of Reinforcement Learning in the Turbulent Regime](https://mlsanity.com/paper/2504.12000/) (2025)

## 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.