mlsanityData to 7 Oct 2026

Comparative analysis of machine learning methods for active flow control

arXiv 2202.11664 ↗First posted 23 Feb 2022physics.flu-dyn cs.LG cs.NEcited by 6

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Abstract (arXiv)

Machine learning frameworks such as Genetic Programming (GP) and Reinforcement Learning (RL) are gaining popularity in flow control. This work presents a comparative analysis of the two, bench-marking some of their most representative algorithms against global optimization techniques such as Bayesian Optimization (BO) and Lipschitz global optimization (LIPO). First, we review the general framework of the model-free control problem, bringing together all methods as black-box optimization problems. Then, we test the control algorithms on three test cases. These are (1) the stabilization of a nonlinear dynamical system featuring frequency cross-talk, (2) the wave cancellation from a Burgers' flow and (3) the drag reduction in a cylinder wake flow. We present a comprehensive comparison to illustrate their differences in exploration versus exploitation and their balance between `model capacity' in the control law definition versus `required complexity'. We believe that such a comparison paves the way toward the hybridization of the various methods, and we offer some perspective on their future development in the literature on flow control problems.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 24 of its 150 references that are in the map. First posted on arXiv 23 Feb 2022.

Builds on

24 of 150 references.

References · 24

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12 more
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Cited by year

Citing papers per year: 2022: 1; 2023: 3; 2024: 2.

6 papers.

  1. 2023Effective control of two-dimensional Rayleigh--B'enard convection: invariant multi-agent reinforcement learning is all you needcited by 25
  2. 2022Deep Learning Closure Models for Large-Eddy Simulation of Flows around Bluff Bodiescited by 11
  3. 2024Multi-agent reinforcement learning for the control of three-dimensional Rayleigh-B'enard convectioncited by 8
  4. 2023Dynamic Feature-based Deep Reinforcement Learning for Flow Control of Circular Cylinder with Sparse Surface Pressure Sensingcited by 7
  5. 2023How to Control Hydrodynamic Force on Fluidic Pinball via Deep Reinforcement Learningcited by 5
  6. 2024Optimal Parallelization Strategies for Active Flow Control in Deep Reinforcement Learning-Based Computational Fluid Dynamicscited by 1