**This meeting will be taking place in Dublin, Ireland**
Advancing space weather forecasting through data-driven models, including machine learning, presents challenges stemming from diverse datasets, metrics, and deployment practices. This meeting will address aspects of applied machine learning, focusing on dataset development, model evaluation, reproducibility, and operational deployment. There will be an emphasis on establishing best practices that align with FAIR data principles, ensuring models are accessible, comparable, and suitable for operational use.
This meeting is particularly timely, given the increasing solar activity and the heightened risks of space weather impacts identified in national risk assessments. The initiative aims to bridge gaps between academia and operational stakeholders. By fostering collaboration, the meeting seeks to drive the adoption of robust machine learning models, enhancing space weather forecasting capabilities and resilience.
Organisational Information
Registration and requests for talks/posters will open shortly. In the meantime, any enquiries about the meeting should also be made to Paul Wright (p.j.wright@exeter.ac.uk) in the first instance.
Organisers:
Dr Paul Wright (DIAS/University of Exeter), p.j.wright@exeter.ac.uk
Dr Sophie Murray (DIAS),
Dr Shane Maloney (DIAS)
Julio Hernandez Camero (UCL MSSL)
Prof. Lucie Green (UCL MSSL)
Location:
Royal Irish Academy, 19 Dawson St, Dublin 2, D02 HH58, Ireland