SWXAI：面向空间天气预报与日物理学的开源多模态可解释性库
SWXAI: A Unified Multimodal Explainability Library for Space Weather Forecasting and Heliophysics
搞太阳物理或空间天气预测的同学可以看看这个库，把图像、时间序列、表格三种数据的可解释性流程统一到一个接口，官方实测写代码量明显变少，跑起来也不慢，文档和示例都齐。
研究人员在 arXiv 发布 SWXAI，一个开源可解释性（XAI）库，统一处理空间天气预报和日物理学中的图像、时间序列和表格三类数据模态。该领域的机器学习模型通常同时输入多视角多波长太阳图像、多变量时间序列和事件级表格数据，而可解释性工作流此前分散在不同库和输出格式之间，复用和复现困难。
SWXAI 为常用的视觉、时间序列和表格学习模型提供统一且可扩展的接口，内置验证、适配和持久化的解释对象，并附带领域示例、可复现 notebook、测试和文档。用户可以检查输出、替换模型或解释器，并把工作流扩展到新的日物理学数据集。
与直接调用后端实现的基准对比显示，SWXAI 大幅减少了生成一个解释所需的代码量，且没有实际的运行时开销。对从事太阳物理和空间天气研究的开发者来说，这意味着可以把散落各处的 XAI 代码收敛到一套可复用接口里，降低多模态科学机器学习流水线的工程成本。
SWXAI: A Unified Multimodal Explainability Library for Space Weather Forecasting and Heliophysics
Space weather forecasting and heliophysics increasingly rely on machine learning models that process heterogeneous inputs such as multi-viewpoint multi-wavelength solar images and rasters, multivariate time series, spatial annotations, and tabular event-level data. However, explainability workflows remain fragmented across libraries, model interfaces, and output formats, limiting reuse and reproducibility. We present SWXAI, an open-source library that unifies explainable AI (XAI) workflows for image, time-series, and tabular data in space weather forecasting and heliophysics. SWXAI provides a common, extensible interface across commonly used vision, time-series, and tabular learning models, with validation, adaptation, and persistent explanation objects. It also includes domain-grounded examples, reproducible notebooks, tests, and documentation, enabling users to inspect outputs, swap models or explainers, and extend workflows to new heliophysics datasets. SWXAI reduces modality-specific engineering overhead and supports comparable, reusable explanations across heterogeneous scientific machine learning pipelines. A benchmark against direct backend implementations shows that SWXAI substantially reduces the code users write to produce an explanation, with no practical runtime cost.
This report is categorized under AITOP's "Papers" desk. Within our cross-regional analytical framework, it directly maps to AI developer toolchains, coding agents, and engineering workflows. Key entities and technical topics under continuous tracking include SWXAI, 可解释性AI, 空间天气, 时间序列.
Source attribution: curated from arXiv cs.LG (@Benedict Antonio Mervyn, Anli Ji, Manolis K. Georgoulis, Berkay Aydin).
