Project Grant 2504439
- This Project Grant award of $281,836 from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) supports a collaborative research project focused on developing innovative techniques to optimize the use of deep learning for computational wave imaging. The project aims to address challenges of data scarcity and improve the generalizability of deep learning models for solving real-world problems in diverse scientific and engineering fields, such as...
- This $400,000 federal Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports research to develop a principled and unified mathematical framework for deep learning on low-dimensional data structures. The project aims to bridge the gap between theory and practice of deep learning by designing "white-box" deep neural networks using unrolled optimization schemes to maximize information gain in...
- This $141,540 Project Grant awarded by the National Science Foundation's (NSF) Mathematical and Physical Sciences (MPS) program supports research at the University of North Carolina at Chapel Hill (UNC-CH) to develop analytical and computational tools for data-driven applications. The project will focus on signal/image recovery from limited data, exploring how to build effective mathematical models and design efficient algorithms to extract insights. Additionally, the project will integrate...
- 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 $249,949 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) supports the development of a rigorous framework to control optimization accuracy in physics-informed deep learning. The project aims to overcome the unpredictability of non-convex optimization in scientific settings where accuracy is critical, such as solving physics-based equations with limited data. By aligning iterative updates with "ideal descent...
- This $343,612 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) will support the development of novel numerical methods and computational tools for solving complex physical problems involving nonlinearities and spatially-varying media. The project aims to create fast, high-fidelity simulation capabilities that can advance scientific understanding and enable new technological innovations, thereby strengthening U.S....
- This Project Grant award of $350,000 from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) supports the development of robust and efficient numerical algorithms for solving nonlinear wave equations. The project aims to advance fundamental research and enable wide-ranging applications in areas such as geophysics, plasma physics, and quantum science, where accurate wave prediction is critical. The primary computational challenges being addressed...
- 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...
- The National Science Foundation (NSF) Division of Mathematical Sciences awarded a $220,000 Project Grant to the International Computer Science Institute (ICSI), a non-profit research organization, under the Mathematical and Physical Sciences program (CFDA 47.049). The project aims to develop resilient and reliable deep learning methods for forecasting complex spatiotemporal ground motion data, with applications in seismology, earth sciences, and other domains. Key technical objectives include...
- This federal Project Grant award of $175,000.00 from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) supports the development of new theoretical frameworks for self-supervised representation learning and their applications in biomedical research. The project aims to advance the theoretical foundations of this machine learning approach and expand its use in biomedical domains where labeled data is scarce. Key anticipated outcomes include new...
This federal Project Grant award of $220,000 from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) aims to optimize the potential of deep learning in computational wave imaging. The key products or services to be delivered under this 3-year award, which runs from September 1, 2025 to August 31, 2028, include: Developing techniques to address data scarcity and improve the generalizability of deep learning models for computational wave imaging, which is critical for scientific fields like materials science, medicine, and geoscience. This includes introducing a novel self-supervised learning method to uncover hidden physical principles within the latent space, as well as integrating physical principles with advanced deep learning models in hybrid learning strategies. The project also emphasizes educational integration, interdisciplinary collaboration, and the sharing of open-source computer codes and datasets to enhance the broader scientific community's ability to conduct research and provide valuable tools for teaching computational and data-enabled science, engineering, and mathematics. The University of North Carolina at Chapel Hill is the awardee, with the research being performed at their facilities in Chapel Hill, North Carolina.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $220.0k | 8/6/25 |