Collaborative Project: Physics-constrained deep learning framework for quantifying surface processes across the Arctic region - PCDL-QuaSPA (GFZ and AWI)

The Arctic is rapidly changing under a warming climate, which affects the polar regions even more than elsewhere on our planet. Many of these changes manifest themselves as landscape features resulting from surface processes of the Earth. We are developing deep-learning models to detect and quantify the changes related to such surface processes in the Arctic region. The outcome will enhance existing measurements and observations, providing an enriched dataset for investigating Arctic surface processes. To understand the underlying physics of this important and complex component of the Earth system, with the aid of available data, we are also developing a physics-informed deep-learning framework to enable efficient and accurate modelling of Arctic surface processes. The results should help researchers and decision makers to predict and assess the impact of climate change in this fragile region on our planet.

This project is one of 17 selected to receive funding from the Helmholtz Association through its Artificial Intelligence Cooperation Unit

Contact at GFZ: Dr. Hui Tang

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