Generative Simulations of The Solar Corona Evolution With Denoising Diffusion : Proof of Concept
This work addresses space weather forecasting for researchers and forecasters by providing a probabilistic and interpretable simulation method, though it is incremental as it adapts existing generative techniques to a new domain.
The authors tackled the problem of simulating the solar corona's evolution for space weather prediction by applying Denoising Diffusion Probabilistic Models (DDPM) to generate 12-hour forecasts from 12-hour input videos, achieving visually realistic outputs and reliable confidence intervals for predictive metrics like EUV peak flux and fluence.
The solar magnetized corona is responsible for various manifestations with a space weather impact, such as flares, coronal mass ejections (CMEs) and, naturally, the solar wind. Modeling the corona's dynamics and evolution is therefore critical for improving our ability to predict space weather In this work, we demonstrate that generative deep learning methods, such as Denoising Diffusion Probabilistic Models (DDPM), can be successfully applied to simulate future evolutions of the corona as observed in Extreme Ultraviolet (EUV) wavelengths. Our model takes a 12-hour video of an Active Region (AR) as input and simulate the potential evolution of the AR over the subsequent 12 hours, with a time-resolution of two hours. We propose a light UNet backbone architecture adapted to our problem by adding 1D temporal convolutions after each classical 2D spatial ones, and spatio-temporal attention in the bottleneck part. The model not only produce visually realistic outputs but also captures the inherent stochasticity of the system's evolution. Notably, the simulations enable the generation of reliable confidence intervals for key predictive metrics such as the EUV peak flux and fluence of the ARs, paving the way for probabilistic and interpretable space weather forecasting. Future studies will focus on shorter forecasting horizons with increased spatial and temporal resolution, aiming at reducing the uncertainty of the simulations and providing practical applications for space weather forecasting. The code used for this study is available at the following link: https://github.com/gfrancisco20/video_diffusion