CAREER: Dimensionality reduction of glacier and ice-sheet processes using deep learning: D — NSF Award to SUNY at Buffalo (NY, $78
The current generation of ice-sheet models underpredicts observed rates of mass loss of the Greenland Ice Sheet, in part because these models are inherently incomplete representations of complex systems. This work aims to reduce this bias by increasing the complexity of glacier processes that are represented in the nex
| Award title | CAREER: Dimensionality reduction of glacier and ice-sheet processes using deep learning: D |
|---|---|
| Award ID | 2441100 |
| Awardee | SUNY at Buffalo |
| City | AMHERST |
| State | NY |
| Amount obligated | $782,557 |
| Principal investigator | Kristin Poinar |
| Program | ANS-Arctic Natural Sciences |
| Start date | 08/01/2025 |
| Abstract | The current generation of ice-sheet models underpredicts observed rates of mass loss of the Greenland Ice Sheet, in part because these models are inherently incomplete representations of complex systems. This work aims to reduce this bias by increasing the complexity of glacier processes that are represented in the next generation of ice-sheet models. Addition and refinement of specific glacier processes to ice-sheet models will improve the accuracy of future sea-level projections, helping world |
| Source | NSF Awards |
$799/mo
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