Project Grant 2510495
- This $199,400 Project Grant award from the National Science Foundation (NSF) under the Mathematical and Physical Sciences program (CFDA 47.049) will fund collaborative research to augment continuous data assimilation and perform equation discovery with applications in geophysics. The research aims to develop more accurate predictive models for complex systems like weather, ocean currents, and groundwater flow by systematically adapting and modifying existing physically derived models using...
- This $193,155 three-year Project Grant from the National Science Foundation's Mathematical and Physical Sciences program (CFDA 47.049) supports research at Brigham Young University to develop a mathematical framework describing how sparse network structures can effectively process information and aggregate it in ubiquitous real-world network patterns. The project aims to advance understanding of how network topology impacts machine learning algorithms' ability to learn from data, starting with...
- This $149,999 Project Grant award from the National Science Foundation's Mathematical and Physical Sciences program (CFDA 47.049) supports research into techniques for identifying hidden or unknown components in nonlinear differential equation models. The research aims to improve modeling and predictive capabilities for complex physical phenomena, such as fluid flows, turbulence, and weather prediction. The award to the Research Foundation of the City University of New York will provide...
- The National Science Foundation (NSF) awarded a $270,000 Project Grant under the Mathematical and Physical Sciences Program (CFDA 47.049) to the Trustees of Indiana University for the project "Bayesian Methodologies for PDE Parameter Estimation: Model Problems, Algorithm Development and Applications." The project aims to develop advanced statistical methods to estimate unknown physical parameters from complex, sparse, and noisy data sets by incorporating first-principles physics into...
- The National Science Foundation awarded a $250,012 Project Grant to the University of Washington under the Mathematical and Physical Sciences Federal Grant Program (CFDA 47.049) with a performance period from September 15, 2025 to August 31, 2028. The grant supports the development of machine learning tools for data assimilation and model calibration applicable to a broad range of physical systems described by ordinary, differential algebraic, or partial differential equations. The project...
- The National Science Foundation (NSF) awarded a $630,763 Project Grant under the Engineering program (CFDA 47.041) to Georgia TECH Research Corp, doing business as the Office of Sponsored Programs. The objective of this 3-year project, running from July 1, 2025 to June 30, 2028, is to develop a systematic approach to modeling multiscale fluid dynamics phenomena. This research aims to advance predictive modeling capabilities for applications such as weather forecasting, climate modeling, fusion...
- This $240,000 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) will support research to develop a new modeling framework that integrates data-driven insights with physical laws, enabling more accurate and consistent spatial predictions. The key objectives are to: (1) establish a theoretical foundation for these hybrid models, (2) implement inference and spatial interpolation using finite element methods and basis...
- This $289,806 Project Grant awarded by the National Science Foundation's (NSF) Mathematical and Physical Sciences Program (CFDA 47.049) will support the development of new computational methods for simulating complex geophysical processes like snowmelt, water runoff, and glacier dynamics. The research at Baylor University will focus on deriving high-order numerical techniques that can accurately represent the inequality constraints (e.g., non-negative water/ice depths) present in these partial...
- The National Science Foundation (NSF) awarded a $250,000 Project Grant under the Computer and Information Science and Engineering (CFDA 47.070) program to the University of Virginia. The grant supports the development of new physics-guided graph network models to capture complex, non-stationary, and poorly observed water dynamics in freshwater ecosystems. Key innovations include new graph-based architectures, continual learning strategies, and model initialization methods that leverage...
- This National Science Foundation (NSF) Project Grant award under the Mathematical and Physical Sciences program (CFDA 47.049) will analyze the computational resources needed for reliable, data-driven decision-making with complex physics-based simulation models, focusing on optimizing the design of renewable tidal energy farms. The $383,840 project, running from August 2024 to July 2027, will create open-source computer code and simulation outputs, and provide training for a PhD student on...
The National Science Foundation (NSF) awarded a $132,000 Project Grant under the Mathematical and Physical Sciences (CFDA 47.049) federal grant program to Brigham Young University (BYU) to develop new methods for accurately modeling and forecasting complex physical systems like weather, ocean currents, and groundwater flow. The project aims to augment existing physics-based models with machine learning techniques to capture physical phenomena more accurately while maintaining scientific interpretability. The investigators will mathematically justify their algorithm for on-the-fly parameter and model discovery, quantify its limitations, and lay the groundwork for further improvements. The research has applications in areas like weather prediction, engine design, and water/food security. The award also supports education and workforce development through student mentoring and industry partnerships. The project period runs from August 1, 2025 to July 31, 2028.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $132.0k | 7/18/25 |