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Fourier Neural Operator for Plasma Modelling
Vignesh Gopakumar · Stanislas Pamela · Lorenzo Zanisi · Zongyi Li · Anima Anandkumar

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in AI for Science: Progress and Promises »
Event URL: https://openreview.net/forum?id=3Ly4MrEXP9 »
Predicting plasma evolution within a Tokamak is crucial to building a sustainable fusion reactor. Whether in the simulation space or within the experimental domain, the capability to forecast the spatio-temporal evolution of plasma field variables rapidly and accurately could improve active control methods on current tokamak devices and future fusion reactors. In this work, we demonstrate the utility of using Fourier Neural Operator (FNO) to model the plasma evolution in simulations and experiments. Our work shows that the FNO is capable of predicting magnetohydrodynamic models governing the plasma dynamics, 6 orders of magnitude faster than the traditional numerical solver, while maintaining considerable accuracy (NMSE $\sim 10^{-5})$. Our work also benchmarks the performance of the FNO against other standard surrogate models such as Conv-LSTM and U-Net and demonstrate that the FNO takes significantly less time to train, requires less parameters and outperforms other models. We extend the FNO approach to model the plasma evolution observed by the cameras positioned within the MAST spherical tokamak. We illustrate its capability in forecasting the formation of filaments within the plasma as well as the heat deposits. The FNO deployed to model the camera is capable of forecasting the full length of the plasma shot within half the time of the shot duration.
Keywords: FNO · Neural operator · surrogate model · plasma · Fusion

Author Information

Vignesh Gopakumar (United Kingdom Atomic Energy Authority)

Vignesh Gopakumar is a machine learning engineer specialising in fusion research with the United Kingdom Atomic Energy Authority. He spends his time building machine learning algorithms to model physics systems that help gain more understanding of the underlying phenomenons. He designs algorithms that help discover anomalies as well as predict malfunction of engineering systems. He’s working on building a model that can be augmented in real time when exposed to different physics principles.

Stanislas Pamela (CCFE - UKAEA)
Lorenzo Zanisi (UK Atomic Energy Authority)
Zongyi Li (Washington University in St. Louis)
Anima Anandkumar (NVIDIA / Caltech)

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