Session 13 - Machine Leaning and statistical inference techniques applied to space weather

Giovanni Lapenta (KULeuven), Enrico Camporeale
Thursday 21/11, 11:15-12:30 & 17:15-18:30

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.

Thursday November 21, 11:15 - 12:30, Rogier
Thursday November 21, 17:15 - 18:30, Rogier

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Talks : Time schedule

Thursday November 21, 11:15 - 12:30, Rogier
11:15Super-Resolving Magnetograms covering 40 years of space weather eventsShneider, C et al.Oral
11:30A machine learning approach for automated ULF wave recognitionBalasis, G et al.Oral
11:45What is the intrinsic dimensionality of the OMNI data? A dimensionality reduction studyTeunissen, J et al.Oral
12:00Forecasting solar wind properties using dimensionality reduction and Self-Organizing MapsAmaya, J et al.Oral
12:15Assessing the predictability of the geomagnetic activity with information theoretical toolsBernoux, G et al.Oral

Thursday November 21, 17:15 - 18:30, Rogier
17:15A gray-box model for a probabilistic estimate of regional ground magnetic perturbations: Enhancing the NOAA operational Geospace model with machine learningCamporeale, E et al.Oral
17:30Multivariate Timeseries Analysis for Solar Flare and Eruption Forecasting: the Unexploited Potential and its Blending with Machine Learning Georgoulis, M et al.Oral
17:45Geomagnetic Kp index forecast using historical values and real-time observationsShprits, Y et al.Oral
18:00Supervised machine learning for flare prediction: the impact of features and of the training set generation process on the forecasting performancesPiana, M et al.Oral
18:15Progress and issues predicting the Dst index using Long Short-Term Memory neural networks.Laperre, B et al.Oral


1Automatic Generation of Daily Space Environment Forecast Text Based on Natural Language GenerationZou, Y et al.p-Poster
2The Rate of Change of the Surface Magnetic Field in the UK: Sources and ForecastingSmith, A et al.p-Poster
3Using LSTM neural networks to forecast geomagnetic stormsPeeperkorn, J et al.p-Poster
4Analyzing big data from space missions and massively parallel simulations within the Horizon 2020 Project AIDALapenta, G et al.p-Poster
5NARMAX approach to the development of spatiotemporal models for space weather forecast.Balikhin, M et al.p-Poster
6Flare Prediction using Deep Learning with multiple wavelength SDO dataKoukras, A et al.p-Poster
7Using dynamical networks to characterize and quantify the evolving spatio-temporal ground pattern of magnetic disturbance seen by 100+ ground based magnetometers with SuperMAG Chapman, S et al.p-Poster
8Flare Prediction using Deep Learning with multiple wavelength SDO dataKoukras, A et al.p-Poster
9Complex systems perspectives pertaining to the research of space weatherBalasis, G et al.p-Poster
10Prediction of extreme flaring events using machine learning methodsPiana, M et al.p-Poster
11Identification of magnetic reconnection regions in PIC simulations with machine learningDupuis, R et al.p-Poster
12Classification of Magnetosheath Jets using Neural Networks, Solar Wind Observations and High-resolution IMF Measurements.Raptis, S et al.p-Poster
13Leveraging the Mathematics of Shape for Machine Learning Prediction of Solar Magnetic EruptionsBerger, T et al.p-Poster
15Convolutional Neural Networks for Automated Detection of ULF Waves in Swarm Time SeriesAntonopoulou, A et al.p-Poster