CEDAR email: Due date correction

[Verkhoglyadova, Olga P (US 335G)]

CORRECTION:
Due date for submission of manuscripts to the topical collection, Applications of Statistical Methods and Machine Learning in the Space Sciences, in Frontiers in Space Physics has been extended to 4 February 2022. For further details, please visit: https://www.frontiersin.org/research-topics/25408<https://urldefense.us/v3/__https:/www.frontiersin.org/research-topics/25408__;!!PvBDto6Hs4WbVuu7!a4z9-CTrSLZB77E57uvfjzIFcxI5bMn1NrrLEU4WfWqRi-ZvIiixLCpvpeDBuFuhOt2wTo9j32illw$>.

Statistical methods have been part of scientific data analysis in the space sciences for decades and machine learning (ML) is becoming an inevitable tool in the analysis of huge volumes of spacecraft data. Data science (DS) and ML are revolutionizing the way scientific problems in the space sciences are conceptualized and addressed, and have shown to be greatly successful in modeling and data analysis. In the wake of the immense volume of data acquired by the numerous spacecraft missions, methods such as time series analysis, segmentation, Bayesian methods, probabilistic inference and surrogate models, to mention a few, are critical for future scientific findings and discoveries. Though ML and deep neural networks are powerful tools for data mining and pattern recognition, and to make predictions, the interpretability of the models built on these techniques have not been explored adequately until recently.

The proposed research topic will be a collection of contributions in the form of original research articles, reviews/mini-reviews, brief reports and commentaries on the present scenario, and scope of statistical methods and ML in the various fields of space sciences such as solar and heliospheric studies, planetary sciences and exoplanets, astrophysics, space weather research and operations, and atmospheric and magnetospheric sciences. We encourage contributions from a wide range of topics including but not limited to: advanced statistical methods, deep learning and neural networks, times series analysis, Bayesian methods, feature identification and feature extraction, physics-based models combined with machine learning techniques and surrogate models, space weather prediction and other domain topics in space sciences where statistical methods and AI are applied, model validation and uncertainty quantification, turbulence and non-linear dynamics in space plasma, physics informed neural networks, information theory and data reconstruction and data assimilation.


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