10 citations · 18 across the 11 of their papers we have counts for
11 papers
Real-time virtual circuits for plasma shape control via neural network emulators: experimental demonstration on MAST Upgrade
Nicola C. Amorisco, Kamran Pentland, Adriano Agnello +12
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…
Real-time virtual circuits for plasma shape control via neural network emulators: integration and testing in the MAST-U PCS
Matthew J. Marshall, Edward Jones, Graham J. McArdle +10
The deployment of advanced, AI-enabled control algorithms in tokamak experiments requires robust integration with existing plasma control system (PCS) architectures and extensive p…
Real-time virtual circuits for plasma shape control via neural network emulators
Alasdair Ross, George K. Holt, Kamran Pentland +7
Reliable position and shape control in tokamak plasmas requires accurate real-time regulation of several strongly coupled shape parameters. The control vectors that disentangle the…
Real-time virtual circuits for plasma shape control via neural network emulators: dynamic validation in closed-loop simulations
K. Pentland, A. Ross, N. C. Amorisco +5
Reliable confinement and stable performance of tokamak fusion plasmas require accurate real-time magnetic shape control. A promising route to reduced latency and increased flexibil…
The FreeGSNKE Pulse Design Tool (FPDT): a computational framework for evolutive plasma scenario and control design
K. Pentland, N. C. Amorisco, A. Ross +8
We present the FreeGSNKE Pulse Design Tool (FPDT), an open-source, Python-based computational framework that enables in silico testing and predictive design of tokamak plasma scena…
Challenges and opportunities for AI to help deliver fusion energy
Adriano Agnello, Helen Brooks, Cyd Cowley +5
There is great potential for the application of AI tools in fusion research, and substantial worldwide benefit if fusion power is realised. However, using AI comes with its own cha…
TokaMind: A Multi-Modal Transformer Foundation Model for Tokamak Plasma Dynamics
Tobia Boschi, Andrea Loreti, Nicola C. Amorisco +13
We present TokaMind, to our knowledge the first open-source foundation model for tokamak plasma dynamics, based on a Multi-Modal Transformer (MMT) and pretrained on heterogeneous d…
TokaMark: A Comprehensive Benchmark for MAST Tokamak Plasma Models
Cécile Rousseau, Samuel Jackson, Rodrigo H. Ordonez-Hurtado +13
Development and operation of commercially viable fusion energy reactors such as tokamaks require accurate predictions of plasma dynamics from sparse, noisy, and incomplete sensors…
Real-Time Applicability of Emulated Virtual Circuits for Tokamak Plasma Shape Control
Pedro Cavestany, Alasdair Ross, Adriano Agnello +5
Machine learning has recently been adopted to emulate sensitivity matrices for real-time magnetic control of tokamak plasmas. However, these approaches would benefit from a quantif…
Validation of the static forward Grad-Shafranov equilibrium solvers in FreeGSNKE and Fiesta using EFIT++ reconstructions from MAST-U
K. Pentland, N. C. Amorisco, O. El-Zobaidi +10
A key aspect in the modelling of magnetohydrodynamic (MHD) equilibria in tokamak devices is having access to fast, accurate, and stable numerical simulation methods. There is an in…
Emulation Techniques for Scenario and Classical Control Design of Tokamak Plasmas
A. Agnello, N. C. Amorisco, A. Keats +5
The optimisation of scenarios and design of real-time-control in tokamaks, especially for machines still in design phase, requires a comprehensive exploration of solutions to the G…