The project was developed by the CIN Researcher Manuel Lacal
Project Summary
The project, conducted as part of my PhD, aimed at integrating data-driven methodologies with physical models to capture the dynamics of geomagnetic storms.
- It was developed a framework combining traditional deterministic models with Physics- Informed Neural Networks (PINNs) to model the temporal evolution of the geomagnetic SYM-H index
- It was used high temporal resolution (1-minute) data from OMNIWeb, including solar wind plasma and interplanetary magnetic field measurements, to study rapid magnetospheric variations.
- Interactions with ESA Φ-lab through literature studies, interactive discussions, and technical presentations, thereby broadening my knowledge and understanding of AI applications in Earth Observation.
Development tools
- Used Python for all code development of practical implementations of PINNs.
- Extracted high-resolution SYM-H index data and relevant solar wind parameters from OMNIWeb (https://cdaweb.gsfc.nasa.gov/).
Development Outputs
- All developed codes, including implementations and examples, are maintained on a private GitHub repository.
- Presentations at Φ-lab.
- Processed datasets used in the analysis are archived with metadata descriptions, ensuring reproducibility.