Neural operator transformers capture bifurcating drift wave turbulence in fusion plasma simulations

Abstract

Self-consistent modeling of turbulence-driven transport is critical for optimizing confinement in magnetically confined fusion plasmas, such as in tokamaks and stellarators. In particular, capturing the long-term co-evolution of turbulence, flow, and background plasma profiles remains computationally challenging. Direct numerical simulation of these multiscale, highly nonlinear processes is often demanding and impractical for real-time control or design optimization. To address this bottleneck, we investigate transformer-based neural operator PDE surrogates for emulating the dynamics of drift-wave turbulence bifurcation mediated by zonal flows, using the modified Hasegawa-Wakatani (MHW) model as a prototypical system. We find that the finetuned neural operator model has excellent performance in capturing the multi-spatiotemporal-scales of MHW turbulence bifurcation and is robust to testing on rare and out-of-distribution dynamics. Specifically, we demonstrate that a single unified model accurately predicts both quasi-steady-state turbulence and a wide range of dynamical transition processes, such as nonlinear saturation, spontaneous suppression of turbulence and the emergence of macroscopic zonal flows, over time horizons vastly exceeding the local turbulence correlation time. This computationally efficient approach establishes a strong foundation for fast, AI-based modeling of complex, multiscale phenomena in magnetized fusion plasmas.

Figures (28)

• Figure 1: Snapshots of the perturbed density $n$, potential $\phi$ and vorticity $\varpi$ for the MHW model in the turbulence saturation stage with background density gradient $\kappa=1.0$ and adiabaticity $\alpha \in \{0.1, 0.3, 1.0\}$.
• Figure 2: Time evolution of the zonal $\epsilon_z$ and perturbed $\epsilon_p$ energy densities for (a) a steady-state simulation with $(\alpha, \kappa) = (1.0, 1.0)$ and (b) a dynamical transition simulation where $\alpha$ is increased instantaneously from 0.1 to 1.0 while $\kappa = 1.0$ remains fixed. Shaded regions represent the 10th to 90th percentiles of spatial variation. Vertical dashed lines roughly delineate different phases of temporal evolution.
• Figure 3: Initial conditions, ground truth and pretrained model prediction after $12\omega_\text{ci}^{-1}$ for an $\alpha=0.4$steady state trajectory.
• Figure 4: Initial conditions, ground truth and pretrained model prediction after $12\omega_\text{ci}^{-1}$ for an $\alpha=0.2$steady state trajectory.
• Figure 5: Pairwise comparisons between the ground truth and pretrained model predictions of 96 random test trajectories, including held-out $\alpha\in\{0.08,0.25,0.75,1.1\}$ (green shaded regions), after $12\omega_\text{ci}^{-1}$ of the total energy $E$, enstrophy $W$, turbulent flux $\Gamma_n$ and resistive dissipation $D_\alpha$.