J. Ireland

Brian Thomas, Robert Candey, Jack Ireland et al.

HDRL held its first HDRL wide strategy meeting in January 2026, pulling in a majority of HDRL member staff and selected HDRL grantees. Just over 40 people attended with a few more online to listen to over 30 lightning talks presented by their colleagues and leaders, participate in several breakout and group discussions, and cast 324 votes on over 50 strategic topic ideas contributed by the attendees. The many ideas generated by attendees can be grouped into seven themes which describe the overall breadth of discussion at the meeting. These themes included: Metadata, Standards, and Semantics Discovery, Search, and Recommendation Modernizing HDRL User experience Modernizing HDRL Staff experience Modernized, Scalable, and Dependable Infrastructure for Compute and Storage Metrics and Impact Assessment Community Engagement and Sustainability Considering the gathered information and the interconnections between the prioritized ideas we concluded that several strategic areas for action exist. This document provides an overview of contributed materials and post meeting strategic analysis.

John C. Dorelli, Chris Bard, Thomas Y. Chen et al.

Traditionally, data analysis and theory have been viewed as separate disciplines, each feeding into fundamentally different types of models. Modern deep learning technology is beginning to unify these two disciplines and will produce a new class of predictively powerful space weather models that combine the physical insights gained by data and theory. We call on NASA to invest in the research and infrastructure necessary for the heliophysics' community to take advantage of these advances.

Gelu Nita, Manolis Georgoulis, Irina Kitiashvili et al.

The authors of this white paper met on 16-17 January 2020 at the New Jersey Institute of Technology, Newark, NJ, for a 2-day workshop that brought together a group of heliophysicists, data providers, expert modelers, and computer/data scientists. Their objective was to discuss critical developments and prospects of the application of machine and/or deep learning techniques for data analysis, modeling and forecasting in Heliophysics, and to shape a strategy for further developments in the field. The workshop combined a set of plenary sessions featuring invited introductory talks interleaved with a set of open discussion sessions. The outcome of the discussion is encapsulated in this white paper that also features a top-level list of recommendations agreed by participants.