An active learning method based on E-SINDy identifies governing ODEs and PDEs accurately with significantly fewer data samples than random sampling across tested systems.
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A CNN-based fusion model trained on multi-instrument solar observations predicts geoeffective CMEs, achieving mean TSS of 0.703 and Brier score of 0.095 via five-fold cross-validation.
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How Low Can You Go? Active Learning for Sparse Model Discovery in the Ultra-Low-Data Limit
An active learning method based on E-SINDy identifies governing ODEs and PDEs accurately with significantly fewer data samples than random sampling across tested systems.