Presentation
Interfacing Machine Learning with Physics-Based Models - Discussion
Presenters
DescriptionWith 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 different
computer 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 and
techniques 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.
computer 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 and
techniques 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.
TimeMonday, June 313:00 - 13:30 CEST
LocationHG E 3
Session Chair
Event Type
Minisymposium
Chemistry and Materials
Climate, Weather, and Earth Sciences
Engineering
Physics
Computational Methods and Applied Mathematics