Learned Coarse Models for Efficient Turbulence Simulation

Understand

Turbulence simulation with classical numerical solvers requires high-resolution grids to accurately resolve dynamics.

• Here we train learned simulators at low spatial and temporal resolutions to capture turbulent dynamics generated at high resolution.
• We show that our proposed model can simulate turbulent dynamics more accurately than classical numerical solvers at the comparably low resolutions across various scientifically relevant metrics.
• Our model is trained end-to-end from data and is capable of learning a range of challenging chaotic and turbulent dynamics at low resolution, including trajectories generated by the state-of-the-art Athena++ engine.