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Solar Wind Speed Estimate with Machine Learning Ensemble Models for LISA
In this work we study the potentialities of machine learning models in reconstructing the solar wind speed observations gathered in the first Lagrangian point by the ACE satellite in 2016--2017 using as input data galactic cosmic-ray flux variations measured with particle detectors hosted onboard the LISA Pathfinder mission also orbiting around L1 during the same years.
Evaluation Metrics for Symbolic Knowledge Extracted from Machine Learning Black Boxes: A Discussion Paper
As opaque decision systems are being increasingly adopted in almost any application field, issues about their lack of transparency and human readability are a concrete concern for end-users.
Clustering-Based Approaches for Symbolic Knowledge Extraction
Opaque models belonging to the machine learning world are ever more exploited in the most different application areas.
Symbolic Knowledge Extraction from Opaque Predictors Applied to Cosmic-Ray Data Gathered with LISA Pathfinder
Machine learning models are nowadays ubiquitous in space missions, performing a wide variety of tasks ranging from the prediction of multivariate time series through the detection of specific patterns in the input data.
