arXiv:2611.10861 · v1
SWXAI: A Unified Multimodal Explainability Library for Space Weather Forecasting and Heliophysics
摘要
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.
作者备注：6 pages, 5 figures. Accepted at CIKM 2026 (Resource Track), Rome, Italy, November 7-11, 2026. Code: https://bitbucket.org/gsudmlab/swxai Docs: https://swxai.readthedocs.io
期刊信息：Proceedings of the 35th ACM International Conference on Information and Knowledge Management (CIKM '26), 2026