LMAG 2023
Workshop on machine Learning, data Mining and data Assimilation in Geospace (LMAG 2023)
Scientific Workshop
Dates: 21 - 24 August 2023
Location: JHU/APL, Laurel MD
Organizers: Misha Sitnov, Harry Arnold, Slava Merkin, Grant Stephens, Juliana Vievering, Simon Wing (APL hosts); Joe Borovsky, Jacob Bortnik, Enrico Camporeale and Bharat Kunduri
Overview
This is an informal workshop to be held at the Johns Hopkins University Applied Physics Laboratory (JHU/APL) during August 21-24, 2023.
The idea of the 1st meeting (LMAG 2020 https://sites.google.com/view/lmag2020/home ) 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.
Main topics :
Understanding Geospace via LMA: How can LMA models be improved given more data? How do they improve our understanding of underlying physics (provide data discovery)? How do they provide nowcasting and forecasting of key Geospace parameters?
Gray-box models: What are the pathways to combining LMA and first-principles approaches? In particular, can LMA be leveraged to facilitate data assimilation in and validation of geospace models?
Explainable / interpretable methods for ML-driven geospace applications.
Comparative LMA: How can LMA applications in other fields (e.g., solar physics, astrophysics, atmospheric physics, mission operations, and information technology) be used to develop and improve LMA methods in Geospace?
LMA and (geo)space missions: How can LMA be applied to formulation of future space missions, e.g., constellation-class missions, AI-enabled data selection, single-probe missions yielding constellation-class mission results? How can they be used to improve the present mission operations and data acquisition?
LMA and information theory: How can the LMA analysis be improved using modern methods of information theory, complexity and uncertainty quantification?
LMA tools, resources and infrastructure (e.g., data sets, facilities, codes, future planning)
