Joint Session 63 Machine Learning and AI for Space Weather

Thursday, 16 January 2020: 1:30 PM-3:00 PM
205A (Boston Convention and Exhibition Center)
Hosts: (Joint between the 17th Conference on Space Weather; and the 19th Conference on Artificial Intelligence for Environmental Science )
Cochairs:
Kelsey Doerksen, Univ. of Western Ontario, Electrical and Computer Engineering, London, ON; Alexander Engell, NextGen Federal Systems, Havre de Grace, MD and David John Gagne II, National Center for Atmospheric Research, Research Applications Laboratory, Boulder, CO

Machine learning creates new opportunities in the Space Weather community for identification, classification, modeling and forecasting. Bridging the gap between the space science and the machine learning community is crucial to working with the enormous datasets collected by space missions. Large, and freely available datasets of in-situ and remote observations collected over several decades of space missions allow for space weather to be an ideal application for machine learning. Utilizing imagery, geomagnetic indices, particle fluxes, magnetograms, and more, one can understand more about complex nature of the solar-terrestiral system in which we live. This session welcomes presentations on the advances in space weather utilizing information theory, neural networks, clustering algorithms, nonlinear auto-regression models, and other nontraditional approaches that take into account the nonlinear and complex dynamics of space weather to improve forecasting, predictions, classification, identification, and uncertainty propagation.

Papers:
1:30 PM
J70.1
Imputation of Geomagnetic Disturbance Fields with Nonlinear Regression based on Synthetic Data
E. Joshua Rigler, USGS, Denver, CO; and D. Lin, K. Pham, and G. Lucas

1:45 PM
J70.2
2:00 PM
J70.3
Developing Deep Learning for Solar Feature Recognition in Satellite Images
Michael Kirk, GSFC, Greenbelt, MD; and R. Attie, J. Stockton, M. Penn, D. Hall, B. Thompson, and J. Willert

2:15 PM
J70.4
2:30 PM
J70.5
Leveraging Topological Data Analysis and Deep Learning for Solar Flare Prediction
Thomas Berger, University of Colorado at Boulder, Boulder, CO; and V. Deshmukh, E. Bradley, J. Meiss, and N. Nishizuka

2:45 PM
J70.6
Emerging Frontiers in Science and Exploration Enabled by AI and Public-Private Partnerships
Madhulika guhathakurta, Ames Research Center, Mountain View, CA

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