# Towards Active Flow Control Strategies Through Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2411.05536 · HTML: https://mlsanity.com/paper/2411.05536/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in RL with diffusion and flow models, one of 15 directions in Deep reinforcement learning methods.
- Builds on 3 of its 18 references that are in the map.
- First posted on arXiv 8 Nov 2024.

## Placed in

- Deep reinforcement learning methods / [RL with diffusion and flow models](https://mlsanity.com/deeprl/rl-with-diffusion-and-flow-models/)

## Builds on (most cited first, 3 of 3)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Using Machine Learning at Scale in HPC Simulations with SmartSim: An Application to Ocean Climate Modeling](https://mlsanity.com/paper/2104.09355/) (2021)
- [Active flow control for three-dimensional cylinders through deep reinforcement learning](https://mlsanity.com/paper/2309.02462/) (2023)

## Built on by (most cited first, 1 of 1)

- [Operator-Theoretic Foundations and Policy Gradient Methods for General MDPs with Unbounded Costs](https://mlsanity.com/paper/2603.17875/) (2026)

## Abstract (arXiv)

This paper presents a deep reinforcement learning (DRL) framework for active flow control (AFC) to reduce drag in aerodynamic bodies. Tested on a 3D cylinder at Re = 100, the DRL approach achieved a 9.32% drag reduction and a 78.4% decrease in lift oscillations by learning advanced actuation strategies. The methodology integrates a CFD solver with a DRL model using an in-memory database for efficient communication between