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 and explainability of the models built on these techniques have not been explored adequately until recently.
Since statistical methods form an integral part of ML techniques, a review of these methods as applied to space sciences is timely and a virtual conference, "Applications of Statistical Methods and Machine Learning in the Space Sciences", was held on 17-21 May 2021 (http://spacescience.org/workshops/mlconference2021.php) that brought together experts in academia and industry to leverage the advancements in statistics, data science, methods of artificial intelligence (AI such as machine learning and deep learning, and information theory to improve the analytic models and their predictive capabilities making use of the enormous data in the field of space sciences. The multidisciplinary conference welcomed students and researchers from all disciplines of space science (solar physics and aeronomy, planetary sciences, geology, exoplanet and astrobiology, galaxies), from the fields of AI, statistics, and data science, and from industry who implement methods of advanced statistics and AI in their research. In addition to keynote lectures and contributed talks/posters, there were discussion sessions designated to handle different topics on each each day with emphasis on the interpretability and explainability of the ML models.
The proposed research topic will be a collection of works presented at this virtual conference and new contributions from the broader scientific community 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, 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.
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 and explainability of the models built on these techniques have not been explored adequately until recently.
Since statistical methods form an integral part of ML techniques, a review of these methods as applied to space sciences is timely and a virtual conference, "Applications of Statistical Methods and Machine Learning in the Space Sciences", was held on 17-21 May 2021 (http://spacescience.org/workshops/mlconference2021.php) that brought together experts in academia and industry to leverage the advancements in statistics, data science, methods of artificial intelligence (AI such as machine learning and deep learning, and information theory to improve the analytic models and their predictive capabilities making use of the enormous data in the field of space sciences. The multidisciplinary conference welcomed students and researchers from all disciplines of space science (solar physics and aeronomy, planetary sciences, geology, exoplanet and astrobiology, galaxies), from the fields of AI, statistics, and data science, and from industry who implement methods of advanced statistics and AI in their research. In addition to keynote lectures and contributed talks/posters, there were discussion sessions designated to handle different topics on each each day with emphasis on the interpretability and explainability of the ML models.
The proposed research topic will be a collection of works presented at this virtual conference and new contributions from the broader scientific community 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, 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.