Project Grant N000142412644

This National Science Foundation (NSF) EAGER (Early-concept Grants for Exploratory Research) Project Grant award, under the NSF Engineering program (CFDA 47.041), provides $300,000 in funding to the University of Southern California (USC) to develop generative artificial intelligence (AI) methods that can learn from both simulations and mathematical equations. The project aims to expand the capabilities of existing large language models, such as OpenAI's ChatGPT, Microsoft's Copilot, and...

The National Science Foundation Division of Computing and Communication Foundations awarded a $300,000 Project Grant to Columbia University for research titled "Collaborative Research: Foundations of Deep Learning: Theory, Robustness, and the Brain" under the Mathematical and Physical Sciences program (CFDA 47.049). The three-year award running from December 2021 through November 2024 will support research into the theoretical foundations of deep learning techniques with a focus on...

The National Science Foundation awarded a $197,000 project grant to the University of Delaware under the Engineering program (CFDA 47.041). The three-year award will fund research to develop a hybrid physics-enhanced deep neural network framework called HyPhy-DNN. HyPhy-DNN aims to provide the performance benefits of deep learning models while incorporating analyzable behaviors and verifiable properties from physical models. This will allow for verifiably safe operation of cyber-physical...

This $450,000 project grant from the National Science Foundation (NSF) Division of Physics will fund the development of machine learning-augmented density functional theory (DFT) tools to enable faster and more accurate quantum simulations of warm dense plasmas. Under the Mathematical and Physical Sciences program (CFDA 47.049), the University of Rochester will receive funding from August 15, 2022 to July 31, 2025. The project aims to advance finite-temperature exchange-correlation...

This Project Grant award from the National Science Foundation's Mathematical and Physical Sciences program (CFDA 47.049) to Texas Tech University System provides $249,394 to develop novel machine learning (ML) models that can accurately approximate kinetic equations used to model non-equilibrium phenomena in physics and engineering. The project aims to create reduced-order ML moment models that can capture the underlying physics while preserving key mathematical properties like hyperbolicity,...

The National Science Foundation (NSF) awarded Arizona State University a 5-year, $196,726 CAREER Program grant under the Computer and Information Science and Engineering (CISE) grant program (CFDA 47.070) to support a research and education program focused on accelerating scientific discovery through physics-informed deep learning models. The project aims to develop physically-consistent dynamics models, deep learning-based symbolic regression algorithms, and multimodal deep learning...

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 $150,000 EAGER: IMPRESS-U Project Grant awarded on January 1, 2024 by the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program will support research at Pennsylvania State University (Penn State) to develop new Random Matrix Theory (RMT) techniques and apply them to improving the performance of Deep Neural Networks (DNNs). The key objectives are to: (1) further advance RMT methods, and (2) leverage these techniques to enhance accuracy, convergence,...

This $281,964 National Science Foundation project grant supports research at the University of Texas at Dallas to investigate mean field games and mean field control in the context of supervised and deep learning. Specifically, the three-year award under the NSF's Mathematical and Physical Sciences program (CFDA 47.049) will examine applying mean field control theory to replace empirical data distributions with joint probability distributions between inputs and outputs in machine learning...

The National Science Foundation awarded New York University a $201,408 Project Grant under the Mathematical and Physical Sciences federal grant program (CFDA 47.049) for the period of September 1, 2021 through August 31, 2023. The grant funds the EAGER: ADAPT project, which will develop AI-enhanced sampling techniques to advance computational methods in lattice field theory and related areas of mathematical, statistical and computational physics. By applying machine learning to sampling...