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.
本条情报归属 AITOP「论文」核心追踪栏目。在跨区域产业观察坐标中，该动态直接关联「AI 开发者工具链、Coding Agent 与工程化实践」研究主线，反映出当前技术演进与商业落地的最新风向。涉及核心实体与技术议题包括：SWXAI、可解释性AI、空间天气、时间序列，读者可通过下方标签进入对应专题页查阅历史脉络与关联对比。
信源核验说明：本文原始线索采编自「arXiv cs.LG」（@Benedict Antonio Mervyn, Anli Ji, Manolis K. Georgoulis, Berkay Aydin），经 AITOP 多维评分管线结构化提炼。
