Zero-dimensional modeling of drift wave turbulence by using Bayesian regression

Abstract We present a Bayesian model selection framework to identify reduced dynamical equations describing the evolution of volume-integrated turbulence energy in magnetically confined plasmas. Using data from two-dimensional Hasegawa–Wakatani simulations with varying adiabatic parameters, we consider a candidate model with polynomial nonlinearities up to ninth order and a coupling term to zonal flow energy. All combinations of these terms are evaluated based on Bayesian model evidence. The optimal model includes only linear growth, quadratic nonlinear saturation, and zonal flow suppression. While subcritical turbulence was not observed in the present dataset, the selection framework successfully excluded redundant higher-order terms, confirming its robustness. Crucially, this approach enables data-driven identification of nonlinear saturation and turbulence-zonal flow coupling, which are often introduced heuristically in conventional models. These results suggest that the proposed framework can serve as a useful tool for constructing interpretable reduced models of plasma turbulence and for examining key mechanisms involved in nonlinear energy regulation.