Prediction of Solar Surface Magnetic Fields Using an AI-based Surface Flux Transport Model

In this study, we develop an artificial intelligence (AI)-based solar surface flux transport (SFT) model. We predict global magnetic field distributions on the solar surface up to the next solar rotation (27.3 days) using deep-learning. Here we train and evaluate our deep-learning model, based on the Pix2PixCC architecture, using data sets of SDO/HMI, SOHO/MDI, and NSO/GONG synoptic maps with a resolution of 360 by 180 (longitude and sine-latitude) from 1996 to 2023. We present results of our model and compare them with those from the persistence model and the conventional SFT model, including the effects of differential rotation, meridional flow, and diffusion on the solar surface. Our AI-based SFT model generates magnetic field distributions for the next solar rotation, better than the conventional SFT model and the persistence model in the quantitative metrics such as RMSE, FSIM, and pixel-to-pixel CC. Our model successfully generates magnetic features, such as the diffusion of solar active regions and the motions of supergranules. Our model also generates small-scale magnetic features better than the conventional SFT models. Using synthetic input data with bipolar structures, we confirm that our model successfully reproduces differential rotation and meridional flow. Finally, we discuss the advantages and limitations of our model in view of magnetic field evolution and its potential applications.