Project Grant 2529112
- 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 Project Grant of $283,099 will support the development of new stochastic optimization methods to calibrate digital twins using large datasets. Awarded under the Engineering program (CFDA 47.041), the grant will fund research at the University of Michigan from January 2023 through December 2025. Specifically, the university researchers will develop computational approaches using statistical theories to guide simulation experiments and identify optimal subsets of...
- The National Science Foundation (NSF) awarded a $429,040 Project Grant under the Mathematical and Physical Sciences (CFDA 47.049) federal grant program to The University of Texas at San Antonio (UTSA). The grant funds a collaborative research project titled "COMPUTATIONALLY EFFICIENT HYPERCOMPLEX VARIABLE-BASED SENSITIVITY METHODS FOR RAPID DIGITAL TWIN MODEL UPDATING." The project aims to develop a new mathematical and computational framework to dramatically accelerate the process...
- This $500,005.00 Project Grant award from the National Science Foundation's Mathematical and Physical Sciences program (CFDA 47.049) aims to develop innovative methodologies for quantifying and controlling rare events in complex systems using digital twins. The project, titled "RAREDT: Rare Event Quantification and Control in Digital Twins," will create digital twin models that explicitly incorporate rare event quantification and control to enhance safety and resilience in areas such...
- 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 Project Grant award for $594,001.00 was provided by the National Science Foundation (NSF) under the Mathematical and Physical Sciences Federal Grant Program (CFDA 47.049) to the University of Utah. The project, titled "MATHEMATICAL UNDERPINNINGS OF POPULATION-BASED CARDIAC DIGITAL TWINS", aims to advance the science and application of cardiac digital twins (CDTs) by leveraging mathematical and statistical methods to enhance the trustworthiness of CDT simulations. Key objectives...
- This $256,000 National Science Foundation Engineering Directorate Project Grant supports the development of a digital twin composition method by Diamond Age Technology LLC. The awardee will develop techniques to efficiently combine disparate data sources describing industrial process facilities into a single digital representation with sub-centimeter accuracy. This digital twin can then be projected into spatial computing environments like virtual and augmented reality. The goal is to encode...
- This federal Project Grant award from the National Science Foundation (NSF) under the Engineering program (CFDA 47.041) provides $199,066 in funding to Miami University to support research on automating the creation of digital twins for manufacturing systems. The research aims to leverage computer vision and deep learning techniques to reduce the manual effort required to build digital twins, while improving their accuracy and interoperability. Key innovations include developing a dataset of...
- The National Science Foundation awarded North Carolina State University $282,315 under the Engineering (47.041) federal grant program to develop new digital twin calibration methods using stochastic optimization techniques. The two-year project will contribute to national prosperity by providing robust estimation approaches for parameter calibration of digital twins with large, complex datasets. Key activities include developing stochastic optimization reconciled with statistical theories to...
- This $493,637 federal Project Grant award, funded by the National Science Foundation (NSF) Engineering program (CFDA 47.041), aims to advance simulation-based manufacturing process digital twin (DT) technologies. The key research goals are to: 1) develop a self-organizing DT framework that continuously validates and calibrates the simulator, 2) create optimal control algorithms for contingency scenarios, and 3) leverage parallel computing for rapid optimization. The research will establish...
This federal Project Grant award for $444,202, provided by the National Science Foundation's (CFDA 47.049) Mathematical and Physical Sciences program, supports research at the University of Utah to develop a novel mathematical and computational framework for dramatically accelerating the calibration and updating of digital twin models. Digital twins are virtual representations of physical systems that enable real-time simulation, monitoring, and prediction of their real-world counterparts, with transformative potential across critical sectors like manufacturing, infrastructure, energy, and defense. However, the time and resources required to update digital models with real-world data is often a significant barrier to their practical deployment. This research aims to overcome this challenge by leveraging advanced hypercomplex mathematics to enable computationally efficient model updating, ultimately supporting faster and more accurate digital twin applications with far-reaching benefits for predictive maintenance, process optimization, and risk mitigation. The project also includes training opportunities for graduate and undergraduate students, contributing to the development of the next generation of scientists and engineers in this emerging field.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $444.2k | 8/5/25 |