Project Grant 2529361
- This federal Project Grant award of $300,000 from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) supports a collaborative research project focused on developing dynamical models, statistical guarantees, and computational methods for sparse sensing in nuclear digital twins. The key products and services to be delivered under this project include: Developing fundamental theory, algorithms, and performance guarantees for optimizing sparse sensor...
- The National Science Foundation (NSF) awarded a $343,340 Project Grant under the Engineering program (CFDA 47.041) to North Carolina State University (NC State) to develop an innovative, adaptive, and resource-efficient sensing system. The project aims to jointly optimize the data acquisition, reconstruction, and inference stages of sensing systems through a learning-based, physics-aware framework. The key objectives include: (1) developing learning-based signal reconstruction techniques, (2)...
- This Project Grant award, valued at $308,750.00 and provided by the National Science Foundation (NSF) under the Mathematical and Physical Sciences (CFDA 47.049) program, supports research investigating market-driven approaches to radio spectrum management that leverage artificial intelligence (AI). The key objectives of this collaborative research project are to develop AI-powered mechanisms for optimizing spectrum allocation, securing spectrum sensing against adversarial manipulations, and...
- This $365,274 Project Grant from the National Science Foundation's Mathematical and Physical Sciences program (CFDA 47.049) will fund the development of novel mathematical techniques and algorithms for designing cost-effective space-time sampling strategies and reconstruction methods for time-evolving functions on graphs. A diverse group of researchers from Northern Illinois University will work to analyze and manage various time-evolving processes sampled under realistic conditions and...
- This Project Grant award of $350,000 from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) will support research to develop new mathematical frameworks and solution algorithms for large-scale stochastic models across a range of application areas. The project will investigate geometric principles and strategies for effectively incorporating randomness into model representations and algorithm design, with a focus on solving equilibrium problems in...
- This $349,969 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) supports the development of advanced algorithms and data analysis methods to enhance the monitoring, control, and overall performance of modern power distribution systems. Specifically, the project aims to integrate and analyze heterogeneous data from power grid infrastructure, such as advanced metering, supervisory control, and micro-phasor measurement systems,...
- This federal Project Grant award, valued at $300,000.00 and funded by the U.S. Department of Energy's National Nuclear Security Administration (NNSA) under the Stewardship Science Grant Program (CFDA 81.112), supports fundamental scientific research critical to maintaining the safety, security, and effectiveness of the U.S. nuclear weapons stockpile. The goal of this three-year project (June 1, 2025 - May 31, 2028) is to utilize a new particle-absorption reaction technique developed by the...
- The National Science Foundation (NSF) awarded a $484,965 project grant under the Engineering (CFDA 47.041) program to New York University (NYU) to develop transformative concepts and methodologies to enhance situational awareness of electric power distribution systems. The project aims to address challenges in integrating distributed renewable energy generation by enabling real-time tracking of distribution system operating states. Key objectives include learning-based continuous-time system...
- This National Science Foundation (NSF) Project Grant award of $200,000 to Kansas State University, under the Mathematical and Physical Sciences program (CFDA 47.049), aims to develop and validate deep-learning-enabled distributed stochastic algorithms to solve large-scale, stochastic security-constrained unit commitment problems within power systems. The project will focus on designing a holistic, three-stage, deep neural network-based machine learning approach, developing solution strategies...
- This Project Grant award of $199,940.00 from the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program is supporting research by Rensselaer Polytechnic Institute (RPI) to develop algorithms that can quickly predict and rectify large-scale disruptions in power systems. The key objectives are: 1) Quickly and reliably detect ambient-level anomalies in power systems and distinguish them from random noise; 2) Localize any detected anomalies; and 3) Determine the...
This Project Grant award, valued at $370,000.00 and provided by the National Science Foundation (NSF) under the Mathematical and Physical Sciences program (CFDA 47.049), focuses on developing fundamental theory, algorithms, and guarantees for sparse sensing optimization in nuclear power subsystems. The key products and services to be delivered include: Dynamical models and information theory of sparse sensing to address challenges in integrating raw sensor data into computational models due to the sparsity of sensors in nuclear reactor components. Optimal regularization and uncertainty quantification methods leveraging control and information theory, statistical mechanics, and uncertainty quantification to enable robust, high-dimensional estimation with guaranteed performance. Open-source community software to be developed within the RAVEN, pySensors, and Nuclear Data Research System frameworks, along with traineeships, software carpentry, and open-source educational curricula. The project aims to enhance national leadership in artificial intelligence and nuclear energy by establishing the critical bidirectional flow of information between virtual models and safety-critical decision-making in physical nuclear energy subsystems through strategic design and budgeting of sparse sensors.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $370.0k | 8/5/25 |