most citedReview of Machine Learning Models for Solar Energetic Particle Prediction

1 citations · 1 across the 5 of their papers we have counts for

collaborators

5 papers

astro-ph.SR2026

A multi-source dataset for solar energetic particle forecasting

Yian Yu, Yang Chen, Lulu Zhao +3

Solar energetic particle (SEP) event forecasting often involves integrating heterogeneous observations that differ in cadence, temporal coverage, format, and historical availabilit…

astro-ph.SR2026

Realtime forecasting of solar energetic particle event and proton flux using multi-source solar observations and multi-task deep learning

Yian Yu, Yang Chen, Lulu Zhao +3

Solar energetic particle (SEP) events, defined by proton flux exceeding 10 pfu in the > 10 MeV channel, pose major risks to spacecraft operations, astronaut safety, and high-latitu…

astro-ph.SR2026★ 1 cited

Review of Machine Learning Models for Solar Energetic Particle Prediction

Spiridon Kasapis, Pouya Hosseinzadeh, Kathryn Whitman +73

Solar energetic particle (SEP) events have attracted increasing attention due to their significant radiation hazards for aviation, spacecraft electronics, and human missions beyond…

stat.AP2026

Stable Multivariate Functional Time Series Prediction for Major Geomagnetic Indices

Yian Yu, Shasha Zou, Tuija Pulkkinen +1

High-resolution scientific data, such as geomagnetic index streams, often exhibit complex temporal dependencies that can be modeled through functional data analysis. Conventional f…

physics.space-ph2025

Solar Energetic Particle Forecasting with Multi-Task Deep Learning: SEPNET

Yian Yu, Yang Chen, Lulu Zhao +3

Solar energetic particle (SEP) events pose severe threats to spacecraft, astronaut safety, and aviation operations. Accurate SEP forecasting remains a critical challenge in space w…