Minisymposium Presentation

With the recent rise of machine (ML) and deep learning there have been several efforts to incorporate these techniques into numerical models. Doing so presents a number of challenges however, including but not limited to: framework and language interoperation; ensuring physical compatibility, stability, and constraints; portability and generalisation of models outside their training domain; understanding biases and uncertainties; and the efficient use of differentcomputer architectures. The three invited talks in this minisymposium present progress that has been made across a range of scientific domains whilst also discussing challenges faced andtechniques to tackle them. This discussion session is a chance to reflect on the common ground between these talks, and what can be learnt from one-another. We welcome anyone who is using ML components in their work with interesting stories to share, and anyone interested in incorporating ML into their work who wishes to learn more. This session, and the minisymposium as a whole, is an opportunity to meet others in the domain.