Reinforcement-learning control of turbulence transition in the modified Hasegawa-Wakatani system

[2608.27845] Reinforcement-learning control of turbulence transition in the modified Hasegawa-Wakatani system Skip to main content Search Submit Donate Log in Physics > Plasma Physics arXiv:2608.27845 (physics) [Submitted on 28 Aug 2026] Title: Reinforcement-learning control of turbulence transition in the modified Hasegawa-Wakatani system Authors: Luning Sun , Ben Zhu , Xin-Yang Liu , Deepak Akhare , Jian-Xun Wang View PDF HTML (experimental) Abstract: Control of plasma turbulence remains a long-standing challenge in magnetically confined fusion research. Here, we explore a reinforcement-learning (RL) approach for bidirectional control of the turbulence-zonal-flow transition in the modified Hasegawa-Wakatani system, a minimal model of electrostatic drift-wave turbulence. Within our model, a weak zonal drag damps the otherwise long-lived zonal structures, giving them a finite lifetime and restoring the drive-damping balance required for repeatable transitions within finite control episodes; meanwhile, actuation is applied through a spatially distributed Gaussian source field under a time-weighted budget constraint on actuation costs. The plasma model is then coupled to CNN-based soft actor-critic and twin-delayed deterministic policy-gradient agents through a GPU-native JAX solver that is highly optimized for fast online training. In the turbulence-suppression task, the learned budget-aware schedule achieves the lowest time-integrated turbulent flux for a given consumed budget, outperforming both constant and linearly decreasing baselines across all tested unseen initial conditions. In the inverse zonal-break task, the agent discovers an up-down antisymmetric actuation pattern that induces radial $E\times B$ convection, disrupts the zonal structure, and sustains the turbulent state. A physics-informed warm-buffer initialization facilitates this discovery, as random exploration alone struggles to locate the narrow optimal manifold within the vast action space. These results demonstrate reinforcement learning as a practical trajectory optimizer for nonlinear plasma dynamics, such as turbulence control, and highlight the importance of physics guidance in such applications. Comments: 28 pages, 12 figures Subjects: Plasma Physics (physics.plasm-ph) Cite as: arXiv:2608.27845 [physics.plasm-ph] (or arXiv:2608.27845v1 [physics.plasm-ph] for this version) https://doi.org/10.48550/arXiv.2608.27845 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Ben Zhu [ view email ] [v1] Fri, 28 Aug 2026 02:25:52 UTC (1,768 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Current browse context: physics.plasm-ph < prev | next > new | recent | 2026-08 Change to browse by: physics References & Citations SciX Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab)

Related documents

• Reinforcement-learning control of turbulence transition in the modified Hasegawa-Wakatani system#779756
• STORM: Scrape-off layer turbulence in tokamak fusion reactors#495393
• Physics-Informed Kolmogorov-Arnold Networks for Grad-Shafranov Tokamak Equilibria#1050038
• Deep Reinforcement Learning for Adaptive Mesh Refinement: A Training Parameter Study#1071578
• Replacing Tunable Parameters in Weather and Climate Models with State-Dependent Functions using Reinforcement Learning#609856