This $500,000 Project Grant award from the National Science Foundation's Engineering program (CFDA 47.041) supports research at Northeastern University to advance computational and data-enabled science and engineering. The project aims to develop a theoretical foundation for "Mechanics Informatics" - a new approach to learning material properties from a single, optimized mechanical test rather than requiring many tests. This will enable more efficient and cost-effective design of...
The National Science Foundation (NSF) awarded a $190,402 Project Grant under the Engineering program (CFDA 47.041) to The Trustees of the Stevens Institute of Technology in Hoboken, New Jersey. This collaborative research project develops a novel adaptive data collection framework to enable reliable data collection under severe time and resource constraints for automated post-disaster rapid damage assessment. The system leverages a Bayesian probabilistic modeling approach to dynamically...
The National Science Foundation (NSF) awarded a $291,349 Project Grant to the University of Alabama under the Engineering program (CFDA 47.041) to develop and apply advanced machine learning force fields to simulate nanoparticle catalysts under realistic reaction conditions. The goal is to elucidate the catalytic active sites and how nanoparticle shapes evolve during catalytic processes. This research will help enable more sustainable chemical manufacturing by improving the computational...
This Project Grant award, valued at $500,000.00 and effective from March 1, 2025 to February 28, 2030, was provided by the National Science Foundation (NSF) Division of Civil, Mechanical, and Manufacturing Innovation under CFDA program 47.041 - Engineering. The funding supports fundamental research at the University of Alabama in Huntsville (UAH) focused on developing a new theoretical foundation for how engineers formulate, analyze, and validate engineering problem spaces. The research aims...
This $600,000 Project Grant award from the National Science Foundation's (NSF) Engineering program (CFDA 47.041) will support research to develop an experimental-computational framework for characterizing damage mechanisms in advanced fiber-reinforced composite materials. The primary objectives are to leverage digital image correlation and discontinuous finite element methods to reconstruct the full-field evolution of material properties and damage in mesostructured composites. This research...
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...
The National Science Foundation (NSF) awarded a 3-year, $290,000 Project Grant under its Mathematical and Physical Sciences program (CFDA 47.049) to the University of Maryland, Baltimore County (UMBC) to conduct research on predicting, suppressing, and optimizing elastic instability in three engineering applications: bridge decks, piezoelectric energy harvesters, and aircraft/projectile paneling. The research will focus on analyzing partial differential equation models of flexible plates to...
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...
The National Science Foundation (NSF) awarded a $399,916 Project Grant under its Engineering program (CFDA 47.041) to the University of Nevada, Reno (UNR) to fund research on developing ultrastrong and ultraelastic metallic alloys using artificial intelligence (AI) enabled automated design. The goal of this 16-month project is to leverage AI, computational modeling, and experimental tools to rapidly design, synthesize, and test new metallic alloy compositions that can withstand extreme stress...
This $200,453 project grant awarded by the National Science Foundation's Engineering program (CFDA 47.041) will develop a new physics-informed deep learning (PIDL) framework to tailor the multidirectional mechanical properties of advanced composite materials. The research will create a data-efficient and physically interpretable surrogate model to understand the relationship between a composite material's architecture and its bulk mechanical properties. This will enable the inverse design and...
The National Science Foundation (NSF) Office of Advanced Cyberinfrastructure awarded a $127,390 Project Grant to the University of Alabama under the Computer and Information Science and Engineering (CFDA #47.070) program. This grant will fund the development of a data-driven closed-loop platform for optimal design of deployable pin-jointed (DPJ) structures, which have applications in aerospace, civil, and biomedical engineering. The key products of this project include: 1) a novel stochastic method for determining the initial equilibrium configuration of DPJ structures, and 2) a new computational modeling technique for DPJ structures based on machine learning and non-destructive testing. This closed-loop platform aims to enhance the performance, safety, and longevity of DPJ structures across various industries. The project will also train engineering students in structural design, computational modeling, and experimental testing.