Project Grant 2450667
Award Date
7/1/25
Completion Date
6/30/28
Dollars Obligated
$631K
Federal Grant Program
47.041
Assistance Type
Project Grant
Place of Performance
Atlanta, GA 30318, USA
Similar Awards
This Project Grant award from the National Science Foundation's (NSF) Engineering program (CFDA 47.041) provides $599,972 to Georgia Tech Research Corporation (Georgia Tech Research Corp) to develop a new approach to multifidelity scientific machine learning for engineering design. The research aims to create machine learning models that can effectively leverage both high-fidelity and low-fidelity computational simulations to generate high-accuracy design predictions at low computational cost....
This $300,000 Project Grant from the National Science Foundation's Division of Mathematical Sciences, under the Mathematical and Physical Sciences program (CFDA 47.049), will support the development of next-generation mathematical and algorithmic tools to address two key issues in applying machine learning to statistical modeling of time-evolving complex systems: a shortage of informative training data and the high computational costs of high-dimensional problems. Specifically, the...
The National Science Foundation awarded a $205,215 Project Grant to the Georgia State University Research Foundation Inc. under the Engineering federal grant program (CFDA 47.041). The award will support research developing a theoretical scaling framework for dispersion in magnetohydrodynamic turbulence through May 2025. Specifically, the awardee will leverage modern theoretical techniques to extend Richardson's 1926 theory of dispersion for hydrodynamic fluids to magnetohydrodynamic settings....
The National Science Foundation (NSF) provided a $400,000 Project Grant from its Engineering program (CFDA 47.041) to Tufts University for a 3-year collaborative research effort to develop new techniques for modeling complex cyber-physical systems. The project aims to combine data-driven machine learning approaches with physics-based modeling to create abstract yet quantitative models that can improve human interaction with engineered systems, including critical infrastructure like energy...
This $297,881 federal Project Grant award from the National Science Foundation (NSF) Engineering program (CFDA 47.041) supports collaborative research to advance the theory and algorithms for distribution control. The research aims to enable precision manufacturing of materials with desired properties and coordination/control of large autonomous agent swarms, promoting national prosperity and welfare. The project will address key challenges in nonlinear agent dynamics, inter-agent...
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 (NSF) Mathematical and Physical Sciences Program (CFDA 47.049) provides $519,851 to The Pennsylvania State University (Penn State) to develop a novel machine learning algorithm that incorporates geometrical constraints to model fluid-structural interactions, such as those seen in large-scale offshore wind turbines and urban air mobility vehicles. The key goals of this 3-year project are to: 1) Create an operator-valued kernel to...
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 $499,624 National Science Foundation project grant supports research at the University of Pittsburgh to develop physics-guided machine learning methods for turbulent flow simulation. Funded under the NSF's Computer and Information Science and Engineering program (CFDA 47.070), the three-year award aims to advance computational fluid dynamics capabilities. Specifically, the university researchers will create a new deep learning model incorporating physical constraints to reconstruct...
This Project Grant award from the National Science Foundation (NSF) Engineering Directorate (CFDA 47.041) provides $523,552 to the University of Texas at Austin to develop advanced computational models for simulating complex turbulent fluid flows. The research aims to create reliable, broadly applicable turbulence models for use in Large Eddy Simulation (LES) to enable more practical and accurate simulations across fields like aeronautics, propulsion, power generation, and wind energy. In...