mlsanityData to 7 Oct 2026

Controlling Rayleigh-B'enard convection via Reinforcement Learning

arXiv 2003.14358 ↗First posted 31 Mar 2020physics.flu-dyn cs.LG cs.SYcited by 25

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Abstract (arXiv)

Thermal convection is ubiquitous in nature as well as in many industrial applications. The identification of effective control strategies to, e.g., suppress or enhance the convective heat exchange under fixed external thermal gradients is an outstanding fundamental and technological issue. In this work, we explore a novel approach, based on a state-of-the-art Reinforcement Learning (RL) algorithm, which is capable of significantly reducing the heat transport in a two-dimensional Rayleigh-B'enard system by applying small temperature fluctuations to the lower boundary of the system. By using numerical simulations, we show that our RL-based control is able to stabilize the conductive regime and bring the onset of convection up to a Rayleigh number Rac ≈ 3 · 104, whereas in the uncontrolled case it holds Rac=1708. Additionally, for Ra > 3 · 104, our approach outperforms other state-of-the-art control algorithms reducing the heat flux by a factor of about 2.5. In the last part of the manuscript, we address theoretical limits connected to controlling an unstable and chaotic dynamics as the one considered here. We show that controllability is hindered by observability and/or capabilities of actuating actions, which can be quantified in terms of characteristic time delays. When these delays become comparable with the Lyapunov time of the system, control becomes impossible.

Key facts. Cited by 25 papers in the map (data through 7 Oct 2026). First posted on arXiv 31 Mar 2020.

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Citing papers per year: 2021: 3; 2022: 3; 2023: 3; 2024: 7; 2025: 3; 2026: 6.

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  1. 2024Wavelet Diffusion Neural Operatorcited by 28
  2. 2023Effective control of two-dimensional Rayleigh--B'enard convection: invariant multi-agent reinforcement learning is all you needcited by 25
  3. 2024SINDy-RL: Interpretable and Efficient Model-Based Reinforcement Learningcited by 24
  4. 2021Physics-informed Dyna-Style Model-Based Deep Reinforcement Learning for Dynamic Controlcited by 22
  5. 2022Data-driven control of spatiotemporal chaos with reduced-order neural ODE-based models and reinforcement learningcited by 16
  6. 2021Hybrid analysis and modeling, eclecticism, and multifidelity computing toward digital twin revolutioncited by 14
  7. 2021Thermal Control of Laser Powder Bed Fusion Using Deep Reinforcement Learningcited by 11
  8. 2022Multi-fidelity reinforcement learning framework for shape optimizationcited by 8
  9. 2024Multi-agent reinforcement learning for the control of three-dimensional Rayleigh-B'enard convectioncited by 8
  10. 2024Parametric PDE Control with Deep Reinforcement Learning and Differentiable L0-Sparse Polynomial Policiescited by 7
  11. 2022Comparative analysis of machine learning methods for active flow controlcited by 6
  12. 2024Koopman-Based Surrogate Modelling of Turbulent Rayleigh-B'enard Convectioncited by 3
13 more
  1. 2025HypeRL: Hypernetwork-Based Reinforcement Learning for Control of Parametrized Dynamical Systemscited by 3
  2. 2023Parallel bootstrap-based on-policy deep reinforcement learning for continuous flow control applicationscited by 2
  3. 2025Control of Rayleigh-B'enard Convection: Effectiveness of Reinforcement Learning in the Turbulent Regimecited by 2
  4. 2025Revisiting Heat Flux Analysis of Tungsten Monoblock Divertor on EAST using Physics-Informed Neural Networkcited by 2
  5. 2024Asynchronous Parallel Reinforcement Learning for Optimizing Propulsive Performance in Fin Ray Controlcited by 1
  6. 2024Optimal Parallelization Strategies for Active Flow Control in Deep Reinforcement Learning-Based Computational Fluid Dynamicscited by 1
  7. 2026Reward hacking in physical reinforcement learning revealed by turbulent drag reductioncited by 1
  8. 2026Heuristic Learning for Active Flow Control Using Coding Agentscited by 1
  9. 2023Genetically-inspired convective heat transfer enhancement in a turbulent boundary layercited by 0
  10. 2026Timescale Separation Enables Deep Reinforcement Learning Control of Rotating Detonation Engine Mode Transitionscited by 0
  11. 2026A perspective on fluid mechanical environments for challenges in reinforcement learningcited by 0
  12. 2026Physics-enhanced reinforcement learning for real-time optimal control of dynamical systemscited by 0
  13. 2026Generalized Kalman filter based temporal difference reinforcement learningcited by 0