Project Grant 2530613
- 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 $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...
- This Project Grant award, funded by the National Science Foundation's (NSF) Engineering program (CFDA 47.041), supports fundamental research to advance the understanding of fluid-coupled granular media behavior. The $409,405 award, effective September 1, 2025 through August 31, 2028, is provided to the Massachusetts Institute of Technology (MIT) to develop new experimental techniques and associated theory to observe the transmission of external loads on both the single-grain scale and the...
- This $280,247 Project Grant award from the National Science Foundation's Geosciences Program (CFDA 47.050) will fund collaborative research by the Massachusetts Institute of Technology (MIT) to develop advanced, AI-powered modeling capabilities for small-scale turbulence within the coupled air-sea boundary layers. The research aims to improve understanding of how surface waves and resulting turbulent processes regulate the exchange of mass, momentum, and energy between the atmosphere and...
- 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 National Science Foundation (NSF) Project Grant award under the Geosciences Program (CFDA 47.050) provides $300,000 in funding to the Massachusetts Institute of Technology (MIT) from November 15, 2024 to October 31, 2027. The award supports the development of machine learning-powered "surrogate models" to increase the computational speed and efficiency of geophysical models used for air pollution and climate research. Key project objectives include: Creating simplified,...
- 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 for $497,026 from the National Science Foundation's Geosciences Program (CFDA 47.050) will fund a collaborative research project at the Massachusetts Institute of Technology (MIT) to develop a multiscale AI-powered ocean emulator. The goal is to investigate the complex nonlinear dynamics of ocean circulation and bridge the gap between understanding and simulating ocean variability across multiple time and space scales. The project aims to construct a physics-based 3D...
- 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 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 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 the diverse solid-fluid interactions in these particle-laden flows. The goal is to discover and validate an elasto-viscoplastic continuum rheology that can accurately represent the stochastic force chains and competing fluid-particle dynamics. The findings will support a range of geoscience applications and improve forecasts of natural hazards that impact lives and infrastructure. The project also provides vertically integrated training for postdocs, graduate, and undergraduate students, and will create publicly accessible AI tools, tutorials, and seminars to disseminate the advances in AI for geosciences. The award period runs from January 15, 2026 to December 31, 2028.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $218.7k | 7/16/25 |