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)
On the optimal prediction of extreme events in heavy-tailed time series with applications to solar flare forecasting -
Victor Verma (University of Michigan)