GyroSwin: 5D Surrogates for Gyrokinetic Plasma Turbulence Simulations
Authors: name: Fabian Paischer, name: Gianluca Galletti, name: William Hornsby, name: Paul Setinek, name: Lorenzo Zanisi, name: Naomi Carey, name: Stanislas Pamela, name: Johannes Brandstetter
Organization: Johannes Kepler University

Nuclear fusion plays a pivotal role in the quest for reliable and sustainable
energy production. A major roadblock to viable fusion power is understanding
plasma turbulence, which significantly impairs plasma confinement, and is vital
for next-generation reactor design. Plasma turbulence is governed by the
nonlinear gyrokinetic equation, which evolves a 5D distribution function over
time. Due to its high computational cost, reduced-order models are often
employed in practice to approximate turbulent transport of energy. However,
they omit nonlinear effects unique to the full 5D dynamics. To tackle this, we
introduce GyroSwin, the first scalable 5D neural surrogate that can model 5D
nonlinear gyrokinetic simulations, thereby capturing the physical phenomena
neglected by reduced models, while providing accurate estimates of turbulent
heat transport.GyroSwin (i) extends hierarchical Vision Transformers to 5D,
(ii) introduces cross-attention and integration modules for latent
3Dleftrightarrow5D interactions between electrostatic potential fields and
the distribution function, and (iii) performs channelwise mode separation
inspired by nonlinear physics. We demonstrate that GyroSwin outperforms widely
used reduced numerics on heat flux prediction, captures the turbulent energy
cascade, and reduces the cost of fully resolved nonlinear gyrokinetics by three
orders of magnitude while remaining physically verifiable. GyroSwin shows
promising scaling laws, tested up to one billion parameters, paving the way for
scalable neural surrogates for gyrokinetic simulations of plasma turbulence.