Project Grant 2530612
- This Project Grant award from the National Science Foundation's Geosciences Program (CFDA 47.050) in the amount of $558,188 awarded on January 15, 2026 aims to advance scientific understanding of the behavior of dense granular media in geoscience applications. The project brings together researchers from geosciences, computer science, and engineering to develop new models that better capture the complex behaviors of particle-laden flows, such as those found in avalanches, volcanic flows, and...
- This Project Grant award of $218,701 from the National Science Foundation's (NSF) Geosciences Program (CFDA 47.050) supports collaborative research to better understand the complex behaviors of dense granular media, such as those found in avalanches, volcanic flows, and river sediments. The research team from the Massachusetts Institute of Technology (MIT) will develop new models that combine laboratory experiments, advanced numerical simulations, and artificial intelligence (AI) to capture...
- This $399,162 Project Grant award from the National Science Foundation's Geosciences Program (CFDA 47.050) aims to develop interpretable, stable, and mass-conserving artificial intelligence (AI) models to improve the computational speed and efficiency of geoscientific models, such as those used for air pollution and climate research. The project will create simpler "surrogate" machine learning models for key components like atmospheric chemistry and wildfire plume rise, allowing for...
- This Project Grant award, funded by the National Science Foundation (NSF) under the Geosciences program (CFDA 47.050), supports research to develop new technologies that integrate advanced artificial intelligence (AI) and machine learning (ML) with established geoscientific domain knowledge to enhance understanding of landslide causality. The $674,291 award, effective October 1, 2024 through September 30, 2027, will enable the Research Foundation of the City University of New York (RFCUNY) to:...
- This Project Grant award from the National Science Foundation's Geosciences Program (CFDA 47.050) provides $300,001 to Trustees of Boston University to develop a foundational Artificial Intelligence (AI) model for advanced seismic data analysis to improve earthquake detection, localization, and characterization. The project aims to revolutionize earthquake science by using AI to unravel patterns in seismic data, leading to more accurate tools for earthquake monitoring and potential prediction....
- This Project Grant award from the National Science Foundation's (NSF) Engineering program (CFDA 47.041) supports research to address critical gaps in understanding, modeling, and predicting soil behavior during transitions from solid-like to fluid-like motion, such as during landslides. The $642,143 award, spanning June 1, 2025 to May 31, 2030, will enable the University of Maine System to: 1) measure the statistical characteristics of particle collisions using discrete element modeling, 2)...
- This Project Grant award, funded by the National Science Foundation's (NSF) Geosciences program (CFDA 47.050), supports a collaborative US-Swiss research effort to study the dynamics of magma reservoirs and the integration of crystals and melt in these "plutonic" systems. The $285,652 award to Brown University will integrate field data, laboratory experiments, and computational modeling to develop a physics-based understanding of how melt and crystals interact within these...
- This National Science Foundation (NSF) Geosciences Program (CFDA 47.050) Project Grant award in the amount of $317,272 will fund the development of new computational methods to simulate and advance the understanding of the dynamic interplay between the Earth's surface and interior processes. The principal awardee, the University of Colorado, will couple two widely used community codes - ASPECT (originally for simulating mantle dynamics) and LANDLAB (for modeling surface processes) - to enable...
- The National Science Foundation awarded a $500,000 Project Grant from the Geosciences program (CFDA 47.050) to the University of Maryland, College Park. The grant will fund a collaborative research project titled "CAIG: Reliable Generative Downscaling for Geoscience Data" from October 1, 2025 to September 30, 2028. The project aims to develop a transformative AI-based framework for generating high-fidelity, physically consistent, and uncertainty-calibrated geoscience data. This will...
- This $162,243 Project Grant award from the National Science Foundation's Geosciences Program (CFDA 47.050) supports collaborative research at the University of Central Florida (UCF) to advance predictive understanding of landscape evolution and erosional extremes under changing climatic conditions. The key objectives are to: (1) extract local geomorphic transport laws driving landscape evolution using large-scale experimental data and novel physics-informed artificial intelligence methods; (2)...
This Project Grant award of $229,128 from the National Science Foundation's Geosciences Program (CFDA 47.050) will fund collaborative research at Brown University to better understand the dynamic behavior of dense granular media in geosciences applications. The key objectives are to develop new models that capture the complex interactions between solids and fluids in particle-laden flows, such as those found in avalanches, volcanic flows, and river sediments. The research will combine laboratory experiments, advanced numerical simulations, and interpretable artificial intelligence (AI) methods to enhance scientific understanding and build public trust in AI-driven tools for forecasting natural hazards and improving Earth system models. The award, which runs from January 15, 2026, to December 31, 2028, supports a vertically integrated training model for postdocs, graduate, and undergraduate students, and the creation of publicly accessible AI tools and educational resources to disseminate the project's advances in AI for geosciences.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $229.1k | 7/16/25 |