ModeCast: One-Shot Prediction of Linear MHD Eigenmodes and Growth Rates in Tokamaks
Abstract
Large-scale magnetohydrodynamic instabilities break plasma confinement, can trigger disruptions, and constrain fusion reactor design; modeling a single configuration against them takes hours with a reference solver such as JOREK, and a design study requires thousands of hours. ModeCast is the first learned surrogate to return the complete linear stability assessment for MHD modes. Trained on a comprehensive dataset of over 45,000 JOREK simulations across thousands of equilibria, it screens stability across toroidal mode numbers n=1-4 and predicts the full spatial structure and growth rate of unstable modes. With a second network of the same architecture replacing the equilibrium solve, the full pipeline from design parameters runs on one GPU up to 1,000,000x faster than JOREK's equilibrium and linear stability runs. The gain comes from building the linear-phase physics into the formulation, objective, and inputs, yielding up to six times more training data. ModeCast misses none of the unstable held-out cases, and its predicted fields reproduce the simulation's measured physics, from magnetic island topology to resistivity trends. On a fixed-geometry resistivity scan outside its training distribution, refitting only the 22k-parameter growth rate head of the 145M-parameter network on four simulation halves the held-out internal kink growth rate error, and fine-tuning the whole network adds little. Because it is differentiable and returns full fields, ModeCast could in the future assimilate experimental measurements, providing a bridge to real devices and accelerating progress toward commercial fusion. Project page: zenithon.ai/research/modecast.
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BibTeX
@misc{higginbottom2026modecast,
title = {{ModeCast: One-Shot Prediction of Linear MHD Eigenmodes and Growth Rates in Tokamaks}},
author = {Alex Higginbottom and Zara Z. Ercan and Abetharan Antony and Guzman Sanchez Gonzalez and Rafael Grazzini Placucci and Armand Kassaï Koupaï and Artur P. Toshev},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.23844},
eprint = {2610.23844},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.23844},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - ModeCast: One-Shot Prediction of Linear MHD Eigenmodes and Growth Rates in Tokamaks
AU - Alex Higginbottom
AU - Zara Z. Ercan
AU - Abetharan Antony
AU - Guzman Sanchez Gonzalez
AU - Rafael Grazzini Placucci
AU - Armand Kassaï Koupaï
AU - Artur P. Toshev
PY - 2026
DA - 2026/10/23
PB - arXiv
DO - 10.48550/arxiv.2610.23844
UR - https://arxiv.org/abs/2610.23844
AB - Large-scale magnetohydrodynamic instabilities break plasma confinement, can trigger disruptions, and constrain fusion reactor design; modeling a single configuration against them takes hours with a reference solver such as JOREK, and a design study requires thousands of hours. ModeCast is the first learned surrogate to return the complete linear stability assessment for MHD modes. Trained on a comprehensive dataset of over 45,000 JOREK simulations across thousands of equilibria, it screens stability across toroidal mode numbers n=1-4 and predicts the full spatial structure and growth rate of unstable modes. With a second network of the same architecture replacing the equilibrium solve, the full pipeline from design parameters runs on one GPU up to 1,000,000x faster than JOREK's equilibrium and linear stability runs. The gain comes from building the linear-phase physics into the formulation, objective, and inputs, yielding up to six times more training data. ModeCast misses none of the unstable held-out cases, and its predicted fields reproduce the simulation's measured physics, from magnetic island topology to resistivity trends. On a fixed-geometry resistivity scan outside its training distribution, refitting only the 22k-parameter growth rate head of the 145M-parameter network on four simulation halves the held-out internal kink growth rate error, and fine-tuning the whole network adds little. Because it is differentiable and returns full fields, ModeCast could in the future assimilate experimental measurements, providing a bridge to real devices and accelerating progress toward commercial fusion. Project page: zenithon.ai/research/modecast.
ER -