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Transferable and Principled Neural Operators for 3D Ocean Forecasting in Regional Seas: A Baltic Sea Case

CORDIS · observation · Publication date unknown

onal function spaces. Once trained, they enable rapid, high-resolution ensemble forecasting while reducing dependence on costly computational infrastructure. This project brings together expertise in machine learning and operational ocean forecasting, enabling a mutually beneficial exchange of knowledge between the researcher and the host institution. At the University of Copenhagen, the fellow will leverage extensive datasets, computational infrastructure, and expert guidance to develop this data-driven model, rigorously evaluate its predictive skill, and benchmark its performance against existing operational ocean forecasting systems. Dissemination activities will target scientific communities where immediate impact is anticipated, including those focused on climate adaptation, natural hazard mitigation, and sustainable ocean management. Together with the host’s capabilities and track record, this ambitious project is well-positioned for success, supporting the fellow’s career development. Operational Ocean Forecasting, Data-Driven Modeling, Ocean–Atmosphere Dynamics, Machine Learn

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recordType

award

status

SIGNED

region

EU

value

263393.28

unit

EUR