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Workshop on machine Learning, data Mining and data Assimilation in Geospace (LMAG2020)
Workshop on machine Learning, data Mining and data Assimilation in Geospace (LMAG2020)
Scientific Workshop
Dates: 21 - 24 September 2020
Location: JHU/APL, Laurel, MD Online because of the pandemic
Organizers: Misha Sitnov, Simon Wing and Slava Merkin (APL hosts); Joe Borovsky, Jacob Bortnik and Enrico Camporeale
Overview
This is an informal virtual workshop to be held at the Johns Hopkins University Applied Physics Laboratory (JHU/APL) during September 21-24, 2020. The idea is to bring together a few dozen of experts most interested and involved in the topic. No registration fee. The total duration planned is 4 days.
The idea of this meeting was inspired by impressive recent progress in three main disciplines, machine learning (L), data mining (M) and data assimilation (A) and the need to better understand the progress in concurrent research directions within different geospace disciplines to use each other’s methods, to combine the results (e.g., to advance Space Weather forecasts) and to find other ways of interaction, synergy and integration.
Tentative science questions :
Tentative science questions :
What progress is/can be achieved with LMA in our understanding of geospace (data discovery)?
What are similarities and differences in application of LMA in geospace compared to other fields (e.g., solar physics, astrophysics, atmospheric physics, mission operations, and information technology)?
How can LMA approaches be used to advance first-principle simulations of geospace? How may they help model-data comparisons?
How can LMA be used to help future missions (e.g., constellation-class missions or to sell single-probe missions as constellation-class ones)?
What other data sets/facilities may be involved in LMA analysis?
How can LMA efforts, made by different groups, be connected together to advance our understanding of geospace?
LMA tools and resources (a bit of training)
Google Sites
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