The session will feature leaders in AI/ML methods for space weather forecasting, demonstrating the promises and opportunities for space science and AI researchers.
Session Chair: Lulu Zhao (University of Michigan - Climate and Space Sciences and Engineering)
- Data Quality Issues in Flare Prediction Using Machine Learning Models - Ke Hu (University of Michigan)
- Predicting Solar Energetic Particle Events Using Machine Learning Algorithms with Flare Features - Chia-Yun Li (University of Michigan)
- An Overview of Surrogate Models for Synthetic White Light Images in the Space Weather Modeling Framework - Aniket Jivani (University of Michigan)
- Regression Estimate Recalibration using Kernelized Stein Discrepancy Scores: Applications in Space Weather - Matthew McAnear (University of Michigan)
- Global Geomagnetic Perturbation Forecasting with Quantified Uncertainty using Deep Gaussian Process - Hongfan Chen (University of Michigan)
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