Accelerating Edge Turbulence Simulations: A Physics-Informed Data-Driven Closure Model for the Hasegawa-Wakatani System

Abstract

Accurate and efficient simulation of edge plasma turbulence is critical for predicting confinement in fusion devices, yet Direct Numerical Simulations (DNS) remain computationally expensive. To address this bottleneck, we present a physics-informed data-driven closure scheme demonstrated on the Hasegawa-Wakatani (HW) system. Instead of using "black-box" neural networks, we leverage the Direct Interaction Approximation (DIA) to derive a rigorous closure structure with six transport coefficients, which are then identified from high-fidelity data using Physics-Informed Neural Networks (PINNs). Crucially, this approach decouples training from simulation: once the coefficients are learned, they are integrated into standard low-resolution solvers, ensuring numerical stability and physical interpretability. The resulting model (EHW-C) reproduces the spectral cascades and particle flux of high-resolution DNS with a tenfold speed-up, successfully capturing complex phenomena like inverse cascades via negative diffusion coefficients. This work serves as a proof-of-concept for developing high-fidelity, accelerated reduced-order models for more complex tokamak turbulence codes.