This Project Grant award for $599,972 from the National Science Foundation's (NSF) Engineering program (CFDA 47.041) supports research at the Georgia Tech Research Corporation to develop a new approach to multifidelity scientific machine learning. The goal is to create machine learning models that can leverage both high- and low-fidelity computational simulations to enable design engineers to rapidly explore high-dimensional design spaces and quantify design-relevant uncertainties across...
Purdue University was awarded a three-year Project Grant totaling $386,562 by the National Science Foundation under the Engineering (47.041) federal grant program. The award will support the development of multiscale computational modeling methods for simulating flow-induced mechanical deformation using nonlocal continuum formulations. Purdue researchers will construct tractable one-dimensional models coupling nonlocal mechanical response to fluid flow, and develop three-dimensional solvers...
This $170,000 Project Grant award from the National Science Foundation (NSF) Division of Mathematical Sciences (CFDA 47.049 - Mathematical and Physical Sciences) supports fundamental research on experimental design and uncertainty quantification frameworks for complex systems. The research aims to develop new statistical surrogate models and sequential experimental algorithms to enhance the efficiency and effectiveness of information collection and decision-making for complex systems in...
This National Science Foundation (NSF) Mathematical and Physical Sciences program Project Grant, with CFDA number 47.049, totaling $203,648.00, provides funding to Purdue University from August 15, 2024 to July 31, 2027. The award supports the development of a mathematical framework to derive tractable reduced-order models that effectively capture the dynamics of complex turbulent systems, such as atmospheric and oceanic flows, controlled plasma fusion, and other engineering applications. The...
This $506,494 National Science Foundation Project Grant under the Engineering (47.041) program will support research at Purdue University developing novel methods for data-driven engineering of service systems. The research aims to leverage large operational data sets and machine learning technologies to facilitate model identification for complex stochastic network models representing service industries. Methods will be extended for model calibration in random and nonstationary environments....
This Project Grant award from the National Science Foundation (CFDA 47.049 - Mathematical and Physical Sciences) provides $529,999 in funding to Purdue University from June 15, 2023 to May 31, 2026. The award supports the development of advanced theories and methods for computer simulation of electronic structure of molecules and materials, with the goal of enabling highly accurate prediction of properties and interpretation of experiments for these systems. The key products and services to be...
The National Science Foundation awarded a $721,021 project grant to the University of Michigan under the Engineering federal grant program (CFDA 47.041) to develop new foundations for multi-fidelity prediction, estimation, and learning under uncertainty in dynamical systems from September 1, 2023 through August 31, 2028. The University will conduct research to enable autonomous systems to estimate the effects of prediction uncertainty on planning and control decisions, with a focus on autonomous...
The National Science Foundation (NSF) Directorate for Engineering (CFDA 47.041) awarded a $431,033 Project Grant to the University of Iowa on Sep 1, 2023 to conduct research on novel computational methods for design optimization under uncertainty with arbitrary dependent probability distributions. The research aims to develop efficient computational algorithms and practical tools for robust and reliability-based design optimization of high-dimensional complex engineering systems subject to...
The National Science Foundation awarded Purdue University a $297,365 Project Grant under the Engineering (47.041) federal grant program. The grant will support research to develop an interpretable machine learning approach to facilitate data-to-knowledge translation for better understanding material integrity issues related to fatigue in additively manufactured components. Specifically, the university will utilize genetic programming based symbolic regression modeling to discover...
The National Science Foundation (NSF) Directorate for Engineering (CFDA Program 47.041) awarded a $344,126 Project Grant to the University of Wisconsin System for a 3-year research project focused on developing new mathematical methods and computational tools to enable the use of complex data formats, such as visual and thermal images, for advanced model predictive control (MPC) systems. The key objectives are to: Integrate concepts from control theory, topology, machine learning, and Bayesian...