Subgrid-scale parametrization of unresolved scales in forced Burgers equation using Generative Adversarial Networks (GAN)

Understand

Stochastic subgrid-scale parametrizations aim to incorporate effects of unresolved processes in an effective model by sampling from a distribution usually described in terms of resolved modes.

• This is an active research area in climate, weather and ocean science where processes evolved in a wide range of spatial and temporal scales.
• In this study, we evaluate the performance of conditional generative adversarial network (GAN) in parametrizing subgrid-scale effects in a finite-difference discretization of stochastically forced Burgers equation.
• We define resolved modes as local spatial averages and deviations from these averages are the unresolved degrees of freedom.