Many fields make use of large-scale scientific codes — numerical weather and climate prediction, atomic and molecular modelling, and plasma physics to name a few. Developments in machine learning have brought opportunities to advance these models through a "hybrid-modelling" approach. For example, using emulators and leveraging data-driven approaches. To achieve this in these large codebases presents a significant challenge on scientific, software, and computational fronts, however.
This two-day workshop held in Cambridge, seeks to bring together researchers, research software engineers, and modelling centres currently working to tackle these challenges to share their recent advances and expertise. We will hear from a range of people about how they have done so, the approaches and tools used, and what they have learnt. Discussion sessions will explore the key current challenges in hybrid modelling looking to establish best practices.
The workshop is intended to facilitate discussion on developing hybrid models for operational use running at scale, and showcasing standardised solutions/tooling. We intend to build bridges and plan for how machine learning methods can be integrated as trusted components into existing large-scale modelling frameworks.
Location
The workshop will be held in Cambridge, UK on the 3rd and 4th September 2025. Activities will be in the Maxwell Centre at West Cambridge. Accommodation (limited capacity) is available at Selwyn College. For more information see the location page.
Registration
Registration is free of charge and via the form linked at the top of this page. We expect this to open in May.
All participants at this event are expected to follow the code of conduct for events of Society of Research Software Engineering. Registration for the event will be taken as an agreement to abide by this guidance, and we reserve the right to remove participants whose actions are in violation.
Contact
The meeting is organised by Joe Wallwork and Jack Atkinson of the Institute of Computing for Climate Science (ICCS) at the University of Cambridge.
Please send any enquiries to iccs[AT]maths.cam.ac.uk