GyroSwin: 5D Surrogates for Gyrokinetic Plasma Turbulence Simulations
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.
68e916ba95e8e6771df38cac
Fabian Paischer
68e916ba95e8e6771df38cad
Gianluca Galletti
68e916ba95e8e6771df38cae
William Hornsby
68e916ba95e8e6771df38caf
Paul Setinek
68e916ba95e8e6771df38cb0
Lorenzo Zanisi
68e916ba95e8e6771df38cb1
Naomi Carey
68e916ba95e8e6771df38cb2
Stanislas Pamela
68e916ba95e8e6771df38cb3
Johannes Brandstetter
GyroSwin: 5D Surrogates for Gyrokinetic Plasma Turbulence Simulations
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.
GyroSwin, a scalable 5D neural surrogate model, captures nonlinear gyrokinetic dynamics and improves heat flux prediction in plasma turbulence simulations.
gyrokinetic equation reduced-order models neural surrogate Vision Transformers cross-attention integration modules channelwise mode separation heat flux prediction turbulent energy cascade nonlinear gyrokinetics scalable neural surrogates