Latent Space Dynamics Learning for Stiff Collisional-radiative Models

Abstract

Collisional-radiative (CR) models describe the atomic processes in a plasma by tracking the population density in the ground and excited states for each charge state of the atom or ion. These models predict important plasma properties such as charge state distributions and radiative emissivity and opacity. Accurate CR modeling is essential in radiative plasma modeling for magnetic fusion, especially when significant amount of impurities are introduced into the plasmas. In radiative plasma simulations, a CR model, which is a set of high-dimensional stiff ordinary differential equations (ODE), needs to be solved on each grid point in the configuration space, which can overwhelm the plasma simulation cost. In this work, we propose a deep learning method that discovers the latent space and learns its corresponding latent dynamics, which can capture the essential physics to make accurate predictions at much lower online computational cost. To facilitate coupling of the latent space CR dynamics with the plasma simulation model in physical variables, our latent space in the autoencoder must be a grey box, consisting of a physical latent space and a data-driven or blackbox latent space. It has been demonstrated that the proposed architecture can accurately predict both the full-order CR dynamics and the critical physical quantity of interest, the so-called radiative power loss rate.