The College of William & Mary received a $229,986 Project Grant award from the National Science Foundation Division of Electrical, Communications and Cyber Systems Engineering program (CFDA 47.041) to develop a hybrid physics-enhanced deep neural network (HyPhy-DNN) framework. The HyPhy-DNN aims to provide the performance benefits of deep neural networks with the analyzability, verifiability, and safety properties of physical models. It will incorporate three architectural innovations:...
Wayne State University was awarded a $296,254 Project Grant from the National Science Foundation Division of Electrical, Communications and Cyber Systems Engineering program to conduct research on physics-model-based neural networks from June 15, 2023 to May 31, 2026. Under this award, the university will develop a hybrid self-correcting physics-enhanced deep neural network framework called HYPHY-DNN. HYPHY-DNN aims to provide the performance benefits of deep learning models while...
The National Science Foundation (NSF) awarded a $600,000 Project Grant under the Computer and Information Science and Engineering (CFDA 47.070) federal grant program to the University of Delaware. The grant will fund a 3-year research project to develop a framework for predictable deep neural network (DNN) inference in autonomous vehicle (AV) perception systems. The key objectives are to: (1) understand the challenges of timing predictability in DNN inference for AVs, (2) design a framework...
The National Science Foundation (NSF) provided a $400,000 Project Grant from its Engineering program (CFDA 47.041) to Tufts University for a 3-year collaborative research effort to develop new techniques for modeling complex cyber-physical systems. The project aims to combine data-driven machine learning approaches with physics-based modeling to create abstract yet quantitative models that can improve human interaction with engineered systems, including critical infrastructure like energy...
This $477,585 federal Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program supports research to develop new methods, algorithms, and software that integrate machine learning/artificial intelligence (ML/AI) with traditional physical knowledge in "physics-informed machine learning" (PIML) models. The project aims to create a cyberinfrastructure that enables the seamless and synergistic integration of ML/AI with...
This Project Grant award of $249,949.00 from the National Science Foundation's (NSF) Mathematical and Physical Sciences Program (CFDA 47.049) supports research to develop a rigorous framework for controlling optimization accuracy in physics-informed deep learning. The project aims to overcome the reliability challenges of non-convex optimization in scientific applications like solving partial differential equations, by aligning iterative updates with mathematically sound "ideal descent...
This National Science Foundation (NSF) Project Grant award under the Engineering program (CFDA 47.041) provides $249,999 to the University of California, Irvine (UCI) to develop a novel neuro-symbolic framework that combines advanced hyperdimensional mathematics with deterministic finite automata and knowledge graphs. The goal is to create robust, interpretable models for efficient, data-driven knowledge transfer across diverse cyber-physical systems. The framework aims to reduce data...
The Trustees of the University of Pennsylvania received a $275,000 project grant from the National Science Foundation's Mathematical and Physical Sciences program (CFDA 47.049) to conduct research on geometrization approaches toward understanding deep learning. Specifically, the three-year award funds research projects analyzing symmetries in trained deep neural networks, examining dynamics of deep learning training, and investigating how deep learning separates data across neural network...
The National Science Foundation (NSF) Engineering Directorate awarded Brigham Young University a $397,875 Project Grant under the Engineering program (CFDA 47.041). The three-year grant, from June 1, 2023 to May 31, 2026, supports the development of new techniques for modeling cyber-physical systems to address challenges with scale and complexity in modern engineering. The project aims to transform human interaction with critical infrastructure such as interconnected energy networks through a...
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...