We would like to call your attention to the AGU session: SM006 Machine Learning in Space Weather at the AGU Fall Meeting to be held during 10 -- 14 December
2018 in Washington DC. The session will bring together talks and posters relevant to the emerging fields of machine learning and data analytics as
applied to space weather research and forecasting challenges. For the full session description, please see below.

Abstracts can be submitted via https://agu.confex.com/agu/fm18/prelim.cgi/Session/45586

The abstract submission site is open until Wednesday, 1st August 23:59 EDT.
Please note that AGU will not accept any late abstracts.

Session ID: 45586
Conveners: Enrico Camporeale (Centrum Wiskunde & Informatica), Ryan McGranaghan
(JPL), Thomas Berger (University of Colorado), and Jacob Bortnik (UCLA)

We look forward to receiving your abstracts.
See you at the AGU Fall Meeting,
Enrico Camporeale, Ryan McGranaghan, Tom Berger, and Jacob Bortnik

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Session Description
SM006 Machine Learning in Space Weather
In the last few years, we have witnessed several ground-breaking results in
Artificial Intelligence, such as image recognition at super-human accuracy,
real-time voice translation, automatic image captioning, and the notorious
defeat of a world champion in the game of Go. Machine Learning is
revolutionizing our world and is rapidly making an impact in science too. A
large amount of data at our disposal puts Space Physics in an optimal position
to capitalize on the recent progress. Potentially, every model in the Space
Weather chain, from the forecast of solar phenomena to the prediction of
geomagnetic disturbances, can be enhanced with a machine-learned approach.
This session will focus on applications of Machine Learning to problems in
Space Weather and Heliophysics. Contributions ranging from black-box models to
data-driven physics-based simulations are welcome, including (but not limited
to) regression and classification problems, dimensionality reduction, automatic
event identification, Bayesian inference, feature extraction, deep learning,
and reinforcement learning.

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