Project Grant W911NF2210271

This Project Grant award, funded by the National Science Foundation (NSF) under the Mathematical and Physical Sciences (CFDA 47.049) program, supports fundamental research to improve the generalization capabilities of digital twin models for complex systems. The $269,187 grant will enable researchers at Smith College to develop hybrid digital twin architectures that combine physics-based and domain-agnostic components, allowing for improved predictive performance across a range of conditions,...

This EAGER (Early-Concept Grants for Exploratory Research) award from the National Science Foundation's (NSF) Engineering program (CFDA 47.041) will develop generative artificial intelligence (AI) methods to learn from computational physics simulations and mathematical equations. The $300,000 project aims to expand the capabilities of large language models, such as OpenAI's ChatGPT and Microsoft's Copilot, to go beyond text-based learning and make predictions on complex, coupled physics problems...

This Project Grant award from the National Science Foundation (NSF) Mathematical and Physical Sciences program (CFDA 47.049) provides $269,979 to the Santa Fe Institute of Science to conduct collaborative research on developing robust digital twin models that can accurately predict the behavior of complex systems under unexpected conditions. The key objectives are to: Investigate the generalization abilities of digital twins by combining mathematical tools from nonlinear dynamics and machine...

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...

This federal Project Grant award, with a total funding amount of $569,051, was provided by the National Science Foundation (NSF) under the Mathematical and Physical Sciences (CFDA 47.049) federal grant program. The award supports a collaborative research project between the University of California, Los Angeles (UCLA) and the University of Utah to develop theoretical foundations for AI-assisted digital twins to integrate scientific data, physical models, and machine learning for complex...

The National Science Foundation (NSF) awarded a $250,000 Project Grant under the Mathematical and Physical Sciences program (CFDA 47.049) to the University of Utah. The grant, with a performance period from October 1, 2024 to September 30, 2027, will fund collaborative research to develop the theoretical foundations for AI-assisted digital twins to integrate scientific data, physical models, and machine learning for complex high-power laser science and engineering. The project aims to enable...

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...

This Project Grant award from the National Science Foundation (NSF) Directorate for Mathematical and Physical Sciences supports the development of advanced artificial intelligence (AI) systems to optimize the search for gravitational wave events accompanied by electromagnetic radiation. The $160,476 award to the California Institute of Technology (Caltech) will fund the creation of an AI agent that can adaptively learn to make optimal decisions for follow-up observations of these rare and...

This $199,066 Project Grant, awarded by the National Science Foundation (NSF) under the Engineering program (CFDA 47.041), supports research to automate the creation of digital twins for manufacturing systems using computer vision and deep learning. The key products and services to be delivered include: Developing a novel framework for fully automated digital twin generation through innovations in computer vision, deep learning, and data labeling. This will significantly reduce the manual effort...

This $618,159 National Science Foundation project grant supports research into developing energy-efficient hardware and software for machine learning and artificial intelligence systems. Funded under the Computer and Information Science and Engineering program, the award supports The Washington University in investigating frameworks using quantum-tunneling dynamic-analog memory devices and novel online learning algorithms. Specific objectives include exploring Fowler-Nordheim dynamic analog...