Related papers: Real-time virtual circuits for plasma shape control via neural network emulators: experimental demonstration on MAST Upgrade

Real-time virtual circuits for plasma shape control via neural network emulators: experimental demonstration on MAST Upgrade

URL: http://arxiv.org/abs/2608.28468v1
Date: Fri, 28 Aug 2026 15:55:27 GMT
Title: Real-time virtual circuits for plasma shape control via neural network emulators: experimental demonstration on MAST Upgrade
Abstract summary: Conventional plasma shape control in tokamaks relies on virtual circuits (VCs) that are computed offline from linearisations.<n>We replace pre-set look up tables with VCs updated in real time using surrogates of the plasma response.<n>These results establish the experimental feasibility of real-time linearisations as a practical extension of conventional plasma shape control in tokamaks.
Score: 1.297149381304018
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Conventional plasma shape control in tokamaks relies on virtual circuits (VCs) that are computed offline from linearisations around a small, tailored number of reference equilibria, and deployed as expertly prepared schedules during the discharge. Here, we report on the first experimental deployment of real-time VCs. We replace pre-set look up tables with VCs updated in real time using surrogates of the plasma response. Both the existing control architecture and the interpretability of VC-based control are retained. Previous work showed that neural network emulators can produce accurate VCs, and validated their performance in closed-loop shape control simulations. Here, we report their first experimental validation on MAST Upgrade (MAST-U). Dedicated experiments spanning different scenarios, including prescribed shape perturbations, feedback-driven divertor-leg motion, and strongly evolving plasma configurations, show that real-time VCs can realise plasma shape control tasks within the MAST-U plasma control system. These results establish the experimental feasibility of real-time linearisations as a practical extension of conventional plasma shape control in tokamaks. The present implementation demonstrates a central step towards a simpler control workflow, in which manually constructed, phased VC schedules are replaced by VCs generated automatically online from a trained surrogate model, without scenario-specific retraining.

Related papers

Real-time virtual circuits for plasma shape control via neural network emulators: integration and testing in the MAST-U PCS [1.5858910567286892]
We describe the integration and testing of neural-network-emulated virtual circuits for plasma shape control within the MAST Upgrade (MAST-U) PCS environment.<n>The neural network models predict the plasma shape using the plasma current, poloidal field coil currents, and plasma profile parameters.
arXiv Detail & Related papers (2026-08-26T09:59:17Z)
Dynamic Plasma Shape Control with Arbitrary Sensor Subsets [0.0]
We present a reinforcement learning agent for tokamak shape control.<n>The agent is trained in NSFsim, a high-fidelity tokamak simulator configured for DIII-D.<n>It achieves a mean shape error of 2.01 cm, and dynamic trajectory following is demonstrated qualitatively in simulation and on the physical device.
arXiv Detail & Related papers (2026-05-15T13:15:45Z)
Real-time virtual circuits for plasma shape control via neural network emulators [0.38186458149494623]
We develop an extensive library of over a million simulated GS equilibria, covering a substantial portion of the MAST-U operational space.<n>We perform extensive verification of the emulated VCs by testing whether they disentangle the control problem.<n>This work establishes the physical validity of emulated VCs as a scalable and general alternative to schedules of precomputed VCs.
arXiv Detail & Related papers (2026-05-14T15:15:43Z)
PVI: Plug-in Visual Injection for Vision-Language-Action Models [8.641415997612066]
VLAs that pair a pretrained VLM with a flow-matching action expert have emerged as a strong paradigm for language-conditioned manipulation.<n>We propose Plug-in Visual Injection (PVI), a lightweight encoder-agnostic module that attaches to a pretrained action expert and injects auxiliary visual representations via zero-d residual pathways.
arXiv Detail & Related papers (2026-03-13T08:16:53Z)
DiffusionHarmonizer: Bridging Neural Reconstruction and Photorealistic Simulation with Online Diffusion Enhancer [62.18680935878919]
We introduce DiffusionHarmonizer, an online generative enhancement framework that transforms renderings into temporally consistent outputs.<n>At its core is a single-step temporally-conditioned enhancer capable of running in online simulators on a single GPU.
arXiv Detail & Related papers (2026-02-27T15:35:30Z)
On-the-Fly VLA Adaptation via Test-Time Reinforcement Learning [61.38273866396522]
Vision-Language-Action models have emerged as a powerful paradigm for general-purpose robot learning.<n>Current methods remain unsuitable for challenging simulated- or physical-world deployments.<n>We introduce a Test-Time Reinforcement Learning for VLAs framework that enables on-the-fly policy adaptation during inference.
arXiv Detail & Related papers (2026-01-11T01:51:30Z)
Real-Time Applicability of Emulated Virtual Circuits for Tokamak Plasma Shape Control [0.40631409309544836]
We quantify the agreement of target displacement from VCs computed via Jacobians of the shape emulators.<n>We address the effect of vessel currents that are not directly measured in real-time and are typically subsumed into effective "shaping currents"<n>We demonstrate that shaping currents can be inferred via simple linear regression on a trailing window of active coil current measurements.
arXiv Detail & Related papers (2025-09-01T21:37:48Z)
FLEX: A Backbone for Diffusion-Based Modeling of Spatio-temporal Physical Systems [51.15230303652732]
FLEX (F Low EXpert) is a backbone architecture for generative modeling of-temporal physical systems.<n>It reduces the variance of the velocity field in the diffusion model, which helps stabilize training.<n>It achieves accurate predictions for super-resolution and forecasting tasks using as few features as two reverse diffusion steps.
arXiv Detail & Related papers (2025-05-23T00:07:59Z)
Test-Time Model Adaptation with Only Forward Passes [68.11784295706995]
Test-time adaptation has proven effective in adapting a given trained model to unseen test samples with potential distribution shifts. We propose a test-time Forward-Optimization Adaptation (FOA) method. FOA runs on quantized 8-bit ViT, outperforms gradient-based TENT on full-precision 32-bit ViT, and achieves an up to 24-fold memory reduction on ImageNet-C.
arXiv Detail & Related papers (2024-04-02T05:34:33Z)
Plasma Surrogate Modelling using Fourier Neural Operators [57.52074029826172]
Predicting plasma evolution within a Tokamak reactor is crucial to realizing the goal of sustainable fusion. We demonstrate accurate predictions of evolution plasma using deep learning-based surrogate modelling tools, viz., Neural Operators (FNO) We show that FNO has a speedup of six orders of magnitude over traditional solvers in predicting the plasma dynamics simulated from magnetohydrodynamic models. FNOs can also predict plasma evolution on real-world experimental data observed by the cameras positioned within the MAST Tokamak.
arXiv Detail & Related papers (2023-11-10T10:05:00Z)
Disentangling Spatial and Temporal Learning for Efficient Image-to-Video Transfer Learning [59.26623999209235]
We present DiST, which disentangles the learning of spatial and temporal aspects of videos. The disentangled learning in DiST is highly efficient because it avoids the back-propagation of massive pre-trained parameters. Extensive experiments on five benchmarks show that DiST delivers better performance than existing state-of-the-art methods by convincing gaps.
arXiv Detail & Related papers (2023-09-14T17:58:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.