GyroSplat: Exact and Differentiable Linear Representations of Plasma Turbulence

Gyrokinetic simulations model the plasma turbulence governing heat transport in magnetically confined fusion devices. Due to their 5D nature, these simulations are computationally expensive and produce massive data volumes, with a single snapshot requiring tens of gigabytes of storage. This makes directly storing intermediate timesteps impractical and motivates the use of (lossy) compression. Recent neural compression approaches achieve favorable rate–distortion trade-offs by augmenting reconstruction objectives with physics-informed losses that preserve derived quantities such as the electrostatic potential and heat flux. However, neural network based representations can be difficult to optimize and refine when additional constraints are imposed. We introduce GyroSplat, a compression method for gyrokinetic fields inspired by Gaussian splatting. GyroSplat represents each plasma field as a weighted sum of independent Gaussian–Gabor splats, yielding a representation that is linear in its coefficients. This linearity enables a staged optimization strategy in which splats are first fitted to minimize reconstruction error, then refined against the potential, and subsequently new splats are added to correct the transport. Moreover, derived quantities admit closed-form expressions in terms of the splat coefficients, enabling efficient refinement via a Gauss–Newton procedure. Experiments on gyrokinetic simulations show that GyroSplat achieves improved trade-offs at matched storage over established compression baselines on derived physical quantities while remaining competitive on field reconstruction. These results highlight Gaussian splatting as an effective and flexible approach to high-fidelity compression of scientific simulation data.