CAREER: Physics-Informed Deep Learning for Ice Dynamics — NSF Award to Stanford University (CA, $738,807)
Harnessing the power of physics-informed artificial intelligence (AI), this CAREER project aims to improve our understanding of how polar ice sheets flow, critical processes that influence global sea-level change. By developing new deep-learning tools that can extract hidden physical properties from satellite data, the
| Award title | CAREER: Physics-Informed Deep Learning for Ice Dynamics |
|---|---|
| Award ID | 2441132 |
| Awardee | Stanford University |
| City | STANFORD |
| State | CA |
| Amount obligated | $738,807 |
| Principal investigator | Ching-Yao Lai |
| Program | ANT Glaciology |
| Start date | 09/01/2025 |
| Abstract | Harnessing the power of physics-informed artificial intelligence (AI), this CAREER project aims to improve our understanding of how polar ice sheets flow, critical processes that influence global sea-level change. By developing new deep-learning tools that can extract hidden physical properties from satellite data, the research addresses challenges in bridging the gap between modeling and observations for predicting future ice-sheet changes. The project will not only advance scientific understan |
| Source | NSF Awards |
$799/mo
Try NSFGrants →