Andreas Kwammen (UiT The Arctic University of Norway) Machine Learning Applications in Space Physics

Andreas Kwammen (UiT The Arctic University of Norway) wird auf unserem IAP-Kolloquium einen Vortrag zum Thema "Machine Learning Applications in Space Physics" halten.

Zusammenfassung:

Observational space physics research is not only limited by data availability, but also by the challenge of processing and interpreting large volumes of radar, satellite, and optical observations. Many tasks, such as classification, feature detection, and parameter extraction, rely on manual inspection or hard-coded algorithms. Automated approaches based on machine learning are now becoming widely used and often outperform traditional methods.

In this presentation, I will give an overview of how machine-learning techniques can be applied to a range of space-physics problems, including auroral image classification, dust detection in Solar Orbiter data, and ionogram scaling. The goal is to demonstrate how widely different tasks can be solved using a similar workflow, highlighting the flexibility of data-driven methods and neural networks as universal function approximators capable of learning complex mappings directly from data.