Project Grant 2441132
- This $411,096 federal Project Grant award was provided by the National Science Foundation's Polar Programs (CFDA 47.078) to The Leland Stanford Junior University (Stanford University). The project aims to utilize machine learning and physics-based models to map and understand how surface meltwater on the Greenland ice sheet reaches the bedrock, which can impact ice sheet dynamics and response to climate change. Key products and services to be delivered include: Development of open-source deep...
- The Leland Stanford Junior University was awarded a $163,981 Project Grant from the National Science Foundation Division of Polar Programs under the Polar Programs federal grant program (CFDA 47.078). The grant will fund collaborative research to investigate four decades of subsurface change beneath the Ross Ice Shelf in Antarctica using historical and modern radar sounding data. The research aims to strengthen fundamental understanding of the polar regions by expanding knowledge of ice shelf...
- This $191,725 National Science Foundation Project Grant supports research at Lehigh University to develop artificial intelligence solutions for navigating and analyzing big data from polar ice sheets. Funded through the NSF's Computer and Information Science and Engineering program, the one-year award will investigate deep learning and hybrid machine learning approaches to automatically mine and understand heterogeneous datasets collected by the Center for Remote Sensing of Ice Sheets....
- This Project Grant award from the National Science Foundation's (NSF) Polar Programs (CFDA 47.078) is funding research to improve the accuracy of forecasts for future sea-level rise. The $782,557 award, active from August 1, 2025 to July 31, 2030, aims to develop efficient artificial intelligence (AI) representations of complex glacier processes to incorporate into ice-sheet models without substantially increasing computational costs. The research will condense three specific glacier processes...
- The National Science Foundation's (NSF) Division of Polar Programs awarded $684,964 under the Polar Programs federal grant program (CFDA 47.078) to Stanford University. The five-year project grant funding will support research and development of customized numerical models to understand the possibility and implications of an internal shear band forming in ice flowing over rough topography. Specifically, the principal investigator will develop a suite of open-source tools to test the hypothesis...
- The National Science Foundation (NSF) awarded a $505,036 Project Grant under the Geosciences program (CFDA 47.050) to the Trustees of Dartmouth College to improve predictions of sea level rise driven by ice mass loss from the Greenland and Antarctic ice sheets. The project aims to combine physical understanding of ice dynamics with artificial intelligence techniques applied to satellite imagery datasets, in order to develop physics-informed models of basal sliding and ice calving processes. This...
- This Project Grant award from the National Science Foundation's (NSF) Polar Programs (CFDA 47.078) is focused on advancing predictive understanding of summertime Arctic sea ice cover. The $261,675 award, running from May 1, 2025 to April 30, 2027, aims to develop new models and forecasting capabilities to predict Arctic sea ice changes, particularly during the summer months when sea ice reaches its minimum extent. The project will leverage advanced machine learning methods to better understand...
- The National Science Foundation Division of Polar Programs awarded a $245,046 Project Grant to Stanford University under the Polar Programs federal grant program (CFDA 47.078) to develop and field test community-driven ice penetrating radar systems for observing complex ice sheet structure and flow. Specifically, the award will support developing a snowmobile-towed radar and unmanned aerial vehicle radar system based on shared software and post-processing tools. Both systems are designed to...
- This $191,868 National Science Foundation project grant supports research to understand surface-to-bed meltwater pathways across the Greenland Ice Sheet. Funded through the Polar Programs grant program (CFDA 47.078), the award will utilize machine learning and physics-based models to map cracks and draining lakes on the ice sheet from satellite imagery. Researchers at the University of Kansas Center for Research will develop open-source artificial intelligence codes to automatically detect ice...
- This $600,397 Project Grant award from the National Science Foundation's Geosciences Program (CFDA 47.050) will fund collaborative research to develop innovative, physics-informed machine learning models for predicting ice dynamics and calving processes that contribute to global sea level rise. The University of Montana will lead this 3-year project, which aims to improve projections of how rapidly the Greenland ice sheet will respond to climate change by combining physical understanding of...
The National Science Foundation (NSF) Polar Programs (CFDA 47.078) awarded a $738,807 Project Grant to The Leland Stanford Junior University (Stanford University) to develop new deep-learning algorithms for inverse modeling of ice sheets and ice shelves. The 5-year project, starting on Sep 1, 2025, aims to improve understanding of how polar ice sheets flow, which is critical for predicting future sea-level changes. The research will leverage physics-informed artificial intelligence to extract hidden physical properties from satellite data, bridging the gap between modeling and observations. The project will make the developed physics-informed AI tools open-source and accessible to the broader glaciology and Earth science research communities, fostering broader impacts through education, training, and the integration of research and teaching.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $738.8k | 8/19/25 |