Registration for ESWW 2021 is now closed.

cd02 Machine Learning and Statistical Inference Techniques

Conveners: Enrico Camporeale (University of Colorado/NOAA Space Weather Prediction Center) and Giovanni Lapenta (Katholieke Universiteit Leuven)

The science of 'making predictions' has been historically based on statistical inference (e.g., frequentist, Bayesian, information criterion-based) and, more recently, on machine learning techniques.

Entire disciplines, such as system identification, data assimilation, information theory, deep learning and uncertainty quantification, have proliferated in the attempt to improve our ability to extract information from data and build predictive models.
Each of these disciplines has been studied and developed in contexts typically unrelated to Space Weather (e.g., quantum mechanics, financial forecasting, astronomy, etc.), yet present powerful new opportunities for our community. Coupled with massively expanded data availability and sophisticated means to analyze voluminous and complex information, the timing is ripe for the Space Weather community to embrace new innovative methodologies.

This session is devoted to contributions to Space Weather specification and prediction that use innovative, multidisciplinary, and, perhaps, unconventional approaches.