Primary Convener:
Xiangning Chu, Laboratory for Atmospheric and Space Physics

Convener:
Sai Gowtam Valluri, NASA Goddard Space Flight Center
Matthew Argall, University of New Hampshire
Banafsheh Ferdousi, Air Force Research Laboratory

Early Career Convener:
Brianna Isola, University of New Hampshire Main Campus

Chair:
Sai Gowtam Valluri, NASA Goddard Space Flight Center
Xiangning Chu, University of California Los Angeles
Matthew Argall, University of New Hampshire
Banafsheh Ferdousi, University of California, Los Angeles

Artificial Intelligence (AI) is rapidly transforming how we explore and understand the Sun-Earth system. This session highlights cutting-edge applications of AI in Heliophysics and showcases how these tools are opening new frontiers in scientific discovery. We invite contributions that demonstrate the applications of AI methods on solving fundamental challenges across solar wind, magnetosphere, ionosphere, thermosphere, and mesosphere. We encourage innovative showcases integrating AI with traditional scientific approaches, such as theory, first-principle simulations, empirical models, and statistical or event-based analyses. Submissions involving data-driven deep learning, interpretable machine learning, physics-informed-neural-networks (PINNs), and AI-guided solutions to partial differential equations (PDEs) are also encouraged. By bridging together AI innovators and domain experts, this session aims to foster a collaborative environment that accelerates discovery and deepens our understanding of Sun-Earth interactions. This session is organized in collaboration with Machine-Learning-based Geospace Environment Modeling (MLGEM) focus group, with the goal of building synergy across the Heliophysics community.

Index Terms
2427 Ionosphere|atmosphere interactions
2431 Ionosphere|magnetosphere interactions
2784 Solar wind|magnetosphere interactions
2788 Magnetic storms and substorms

Neighborhoods:
4. Beyond Earth

Suggested Itineraries:
Space Weather
Science Communications
Machine Learning and AI
Open Science and Open Data