Session

DescriptionMany fields of science make use of large numerical models. Advances in artificial intelligence (AI) and machine learning (ML) have opened many new approaches, with modellers increasingly seeking to enhance simulations by combining traditional approaches with ML/AI to form hybrid models. Examples of such approaches include ML emulation of computationally intensive processes and data-driven parameterisations of sub-grid processes. Successfully blending these approaches presents several challenges requiring expertise from multiple areas: AI, domain science, and numerical modelling through to research software and high performance computing. Whilst hybrid modelling has recently become an extremely active area in Earth sciences, the approach and challenges are in no way specific to this domain. Progress is also underway in materials, fluid mechanics and engineering, plasma physics, and chemistry amongst other fields. This interdisciplinary session on hybrid modelling aims to allow scientific modellers to share techniques and breakthroughs in a cross-domain forum. We will hear from both academia and industry about the tools being developed and techniques being used to push forward on a range of fronts across multiple fields. This will be followed by a discussion session in which attendees are invited to share their own challenges and successes with others.