用扩散模型预测太阳日冕演化，生成带置信区间的预报结果。
Generative Simulations of The Solar Corona Evolution With Denoising Diffusion : Proof of Concept
- 采用改进的UNet结构，融合时空注意力与1D时间卷积建模日冕演化。
- 12小时输入可预测未来12小时演化，每2小时输出一次，保持视觉真实性和随机性。
- 首次实现关键指标（如通量、辐照度）的概率化预测，适合空间天气预报研究者。
太阳磁化日冕驱动多种具有空间天气影响的现象，如耀斑、日冕物质抛射（CME）以及太阳风。因此，模拟日冕的动力学与演化对提升空间天气预测能力至关重要。本文证明了生成式深度学习方法，如去噪扩散概率模型（DDPM），可成功应用于模拟极紫外（EUV）波段观测下的日冕未来演化。模型以12小时活跃区（AR）视频为输入，预测其后续12小时的演化，时间分辨率为每2小时一次。我们提出一种轻量级的UNet主干架构，通过在每个传统2D空间卷积后加入1D时间卷积，并在瓶颈层引入时空注意力机制来适配该问题。模型不仅生成视觉上逼真的输出，还捕捉到系统演化的内在随机性。值得注意的是，仿真能够生成关键预测指标（如日冕的EUV峰值通量和辐照度）的可靠置信区间，为概率化与可解释的空间天气预报铺平道路。未来工作将聚焦于更短预测时长、更高时空分辨率，以降低不确定性并推动实际应用。本研究代码已开源：https://github.com/gfrancisco20/video_diffusion
原文摘要 · Abstract (English)
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
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