CEDAR email: URSI-NRSM 2023 Session: Machine learning techniques for near-earth space sciences (GH), abstracts welcome
Deshpande, Kshitija B.
DESHPANK at erau.edu
Wed Aug 31 19:07:49 MDT 2022
We would like to invite the community to submit an abstract to the special session SS.17 (GH) “Machine learning techniques for near-earth space sciences” at the upcoming URSI-NRSM conference, which will be held in Boulder, CO January 10-14, 2023.
This session seeks contributions from the following topics: ML techniques applied for space weather prediction, studies of the magnetosphere, radiation belts, ionosphere and upper atmosphere. We welcome contributions with applications to (but not limited to) event detection/extraction, noise reduction, classification, inverse problems, dimensionality reduction, feature engineering/extraction, reinforcement learning, physics-informed machine learning and ML learned partial differential equations. A combination between ML techniques and physics-based models is also especially welcome.
The deadline for abstract submissions is September 16, 2022. More information is available at https://nrsmboulder.squarespace.com/<https://nrsmboulder.squarespace.com/>
While USNC-URSI encourages in-person attendance for all presenters, a fully-hybrid meeting is being planned to allow for authors to present remotely, and for all attendees to participate fully in the conference.
We look forward to seeing you in Boulder!
Sessions Co-Chairs: Vijay Harid, Xiangning Chu, and Kshitija Deshpande
Kshitija Deshpande, PhD
Associate Professor of Engineering Physics
Department of Physical Sciences
Embry-Riddle Aeronautical University
Office: COAS 319.01
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