Project Grant 2505987
- This $250,000 Project Grant award from the National Science Foundation (NSF) Mathematical and Physical Sciences program (CFDA 47.049) supports the development of a Unified Neural Operator (UNO) framework for trustworthy operator learning to create computationally efficient and robust surrogate models for scientific applications. The project aims to advance scientific machine learning capabilities, particularly in areas like predicting complex fluid flows and modeling plasma behavior in fusion...
- This Project Grant award from the National Science Foundation (NSF) under the Mathematical and Physical Sciences Federal Grant Program (CFDA 47.049) provides $360,405.00 to the University of New Mexico (UNM) to develop AI/ML-based computational tools enabling novel searches for the physical origin and properties of the invisible elements making up the cosmos. The project aims to determine whether neutrinos have nonstandard interactions that drive cosmic expansion and the growth of large-scale...
- This National Science Foundation (NSF) Integrative Activities (CFDA 47.083) Project Grant award of $299,862 to the University of Nevada, Reno will support research to develop a new framework for integrating machine learning and physics-based computational models to create "digital twins" of dynamic systems. The research aims to address limitations in current hybrid data-driven modeling approaches by embedding neural networks within physics-based models to better account for modeling...
- The National Science Foundation (NSF) awarded a $250,000 Project Grant under the Mathematical and Physical Sciences program (CFDA 47.049) to the University of Utah. The grant, with a performance period from October 1, 2024 to September 30, 2027, will fund collaborative research to develop the theoretical foundations for AI-assisted digital twins to integrate scientific data, physical models, and machine learning for complex high-power laser science and engineering. The project aims to enable...
- This $175,000 federal Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) supports the development of a unified generative prediction and inference framework using diffusion processes, normalizing flows, and transfer learning to model joint distributions of tabular and unstructured data. The key products and services delivered under this award include: Algorithms for domain adaptation, reliability metrics for trustworthy AI,...
- This $307,266 federal Project Grant award from the National Science Foundation's Mathematical and Physical Sciences Program (CFDA 47.049) supports the development of effective computational methods for training neural networks using an Exploration-Exploitation-Determination (EED) framework. The project, conducted by North Carolina State University, aims to address fundamental challenges in training neural networks, which are core components of modern AI models. The key objectives include: 1)...
- This Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program provides $399,998 to the University of Texas at Austin (UT Austin) to develop innovative numerical algorithms that integrate classical numerical schemes and deep learning techniques. The goal is to address complex scientific computing challenges, such as simulating high-dimensional, fully nonlinear differential equations, long-term Hamiltonian system simulations, and...
- This Project Grant award of $148,654 from the National Science Foundation's Mathematical and Physical Sciences program (CFDA 47.049) aims to develop statistical tools to improve the reliability of artificial intelligence (AI) used in real-world applications such as automated decision-making, financial forecasting, and neuroscience research. The research will establish mathematically rigorous methods for uncertainty quantification to build trustworthy AI, with applications including enhancing...
- The National Science Foundation (NSF) awarded a Project Grant titled "REU SITE: DEEP LEARNING FOR DYNAMICAL SYSTEMS IN BIOLOGICAL AND PHYSICAL SCIENCES" under the Mathematical and Physical Sciences program (CFDA 47.049). The $301,326 grant, awarded on July 1, 2025, will support an 8-week summer program over 3 years at the University of Southern Mississippi. The program will prepare 10 undergraduate students annually for the STEM workforce by immersing them in cutting-edge...
- This Project Grant award from the National Science Foundation's (NSF) Division of Mathematical Sciences (CFDA 47.049 - Mathematical and Physical Sciences) supports research to develop novel mathematical models and efficient algorithms for deep learning on large-scale graph-structured data. The $249,999 award, spanning September 2024 to August 2027, aims to produce innovations in areas like graph convolutional networks, graph matching, and graph clustering. The research will involve graduate...
This Project Grant award of $200,000 from the National Science Foundation (NSF) under the Mathematical and Physical Sciences program (CFDA 47.049) supports the development of the Unified Neural Operator (UNO) framework. The UNO project aims to create a unified, theoretically-grounded approach to operator learning, a key scientific machine learning technique that can enable computationally-efficient and trustworthy surrogate models for complex scientific applications such as fluid dynamics and plasma behavior. By marrying the mathematical rigor of traditional methods with the expressivity of modern AI, the UNO framework is expected to produce certifiable, interpretable AI-driven surrogates that advance priorities in artificial intelligence and nuclear energy. The award is funded from September 1, 2025 through August 31, 2028 and is being conducted by Boise State University, a public research university with extensive expertise in scientific research across diverse domains.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $200.0k | 8/6/25 |