Project Grant 2504438
- 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...
- 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 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program provides $122,648 in funding to Iowa State University over a 3-year period from May 1, 2024 to April 30, 2027. The project aims to deliver mathematical innovations that will improve the reliability and time resolution of machine learning algorithms for national security applications, such as rapid detection and classification of potential threats. Key objectives...
- 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 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 $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 $175,000 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) will fund research to develop new statistical and computational methods to enhance the reliability of data analysis in modern, large-scale datasets, particularly in the era of AI. The key areas of focus include: (1) analyzing the robustness of manifold and deep learning algorithms for high-dimensional, noisy, and nonlinear data; (2) developing statistical theory...
- This $131,959 Project Grant awarded by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program supports research conducted by Rutgers, The State University to develop new deep learning training methods that can efficiently scale to utilize high-performance computing (HPC) systems. The key goals are to: 1) Explore techniques like second-order information approximation, computation-communication tradeoffs, and data compression to enhance the speed...
- 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 $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 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 materials science, medicine, and geoscience. Key research activities include integrating physical principles with advanced deep learning models through hybrid learning strategies, and introducing a novel self-supervised learning method to uncover hidden physical principles. The project also emphasizes educational integration, interdisciplinary collaboration, and the sharing of open-source computer codes and datasets to support the broader scientific community. The award is held by Iowa State University of Science and Technology, a leading research institution with extensive experience in delivering specialized scientific research and technical services to federal agencies.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $281.8k | 8/6/25 |