What kinds of problems in solar physics and space weather are well solved by deep learning?

In this talk,  I introduce our recent deep learning applications to solar and space weather data. We have successfully applied novel deep learning methods to the following applications: (1) image translations between solar images such as solar farside magnetograms, (2) improvement of  empirical models, (3) inversion problem, (4) super resolution, (5) pixel-to-pixel translation, (6) reduction of simulation times, (7) 2D to 3D conversion, and (8) space weather forecasting. We also discuss the applications of symbolic regression and GPT for space weather.