Project Grant FA95502410069

This three-year, $240,000 Project Grant from the National Science Foundation's Division of Electrical, Communications and Cyber Systems, under the Engineering program (CFDA 47.041), will support collaborative research between Columbia University and the University of Pennsylvania on scalable and communication-efficient learning-based distributed control. The researchers will develop a foundational and integrated theory of distributed learning-enabled control and approximated distributed...

This three-year $240,000 Project Grant from the National Science Foundation's Division of Electrical, Communications and Cyber Systems, under the Engineering program (CFDA 47.041), will support research to advance the practical application of distributed learning-enabled control systems. The University of Pennsylvania and Columbia University will collaborate on developing foundational theory and integrated approaches for scalable and communication-efficient distributed control. This includes...

This $225,000 National Science Foundation (NSF) Project Grant award under the CFDA 47.041 Engineering program aims to develop a physics-informed, real-time optimal power flow model using machine learning techniques. The project seeks to address gaps in providing close to optimal solutions for power plant outputs while considering practical dynamical constraints to avoid frequency fluctuations and grid instabilities. The key scientific and engineering contributions include: (1) advancements in...

The National Science Foundation awarded a $225,000 Project Grant under the Engineering (47.041) federal grant program to the University of Colorado from July 1, 2023 to June 30, 2026. The University will develop a physics-informed real-time optimal power flow model using machine learning techniques to provide close to optimal solutions for power plant outputs while considering dynamic constraints to avoid grid instabilities. Key activities include advancing techniques combining...

This Project Grant from the National Science Foundation's $200,000 Engineering program (CFDA 47.041) will fund research at the University of California, San Diego to develop a new data-driven power systems control framework with stability guarantees. The three-year award beginning March 2022 aims to design reinforcement learning algorithms for inverter-based frequency and voltage control of power grids that provide formal stability through a novel approach bridging Lyapunov control theory and...

This National Science Foundation Project Grant award of $538,633 supports fundamental research in modeling stochastic traffic flows for smart mobility systems through February 2026. Funded under the Engineering program (CFDA 47.041), the award to the University of Maryland, College Park aims to develop machine learning and traffic flow theory to better estimate and predict mobility patterns. The grantee will fuse classical transportation models with learning techniques using transportation...

This $499,624 National Science Foundation project grant supports research at the University of Pittsburgh to develop physics-guided machine learning methods for turbulent flow simulation. Funded under the NSF's Computer and Information Science and Engineering program (CFDA 47.070), the three-year award aims to advance computational fluid dynamics capabilities. Specifically, the university researchers will create a new deep learning model incorporating physical constraints to reconstruct...

This $200,000 National Science Foundation project grant supports the development of data-driven power systems control with stability guarantees. Funded through the NSF Engineering program (CFDA 47.041), the award to Carnegie Mellon University will support three thrusts of collaborative research over a 30-month period ending February 2025. The research aims to design a new framework integrating reinforcement learning algorithms with Lyapunov stability theory to provide stability guarantees for...

The University of California, Berkeley received a $276,684 project grant from the National Science Foundation Division of Electrical, Communications and Cyber Systems to support research titled "COLLABORATIVE RESEARCH: LEARNING-BASED SCALABLE PREDICTIVE CONTROL STRATEGIES FOR HETEROGENEOUS TRAFFIC NETWORKS." The research aims to develop learning-based predictive control strategies for heterogeneous traffic networks and will be conducted from January 1, 2022 to December 31, 2024. The...

This $100,000 National Science Foundation Project Grant supports research at the University of Washington to develop a new data-driven power systems control framework with stability guarantees. Funded under the NSF Engineering program (CFDA 47.041), the research aims to design reinforcement learning algorithms for inverter-based frequency and voltage control of power grids that provide formal stability assurances. Over the two-year period from March 2022 to February 2025, university...