Project Grant 2226348
- 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 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 $275,000 Project Grant from the National Science Foundation's (NSF) Division of Mathematical Sciences under the Mathematical and Physical Sciences (CFDA 47.049) program supports research on optimization algorithms and digital twins constrained by partial differential equations (PDEs) that incorporate data to make decisions resilient to uncertainty. The research will develop: (1) inexact adaptive semismooth Newton and trust-region methods to solve these optimization problems; (2) primal dual...
- This $444,562 Project Grant award from the National Science Foundation (NSF) Engineering program (CFDA 47.041) supports research at Michigan State University to address the digital transformation of the facility management industry. The key objectives are to: Construct a digital twin ecosystem to enhance the role of facility managers through physical and cognitive assistance. Develop a multi-modal user interface to promote effective interaction within the digital ecosystem. Gauge facility...
- This $332,768 National Science Foundation project grant under the Engineering program (CFDA 47.041) will fund the development of a new hybrid pseudo-spectral adjoint algorithm and multidisciplinary design optimization framework at the University of Michigan from September 2022 through August 2025. The algorithm and framework aim to enable the gradient-based MDO of large-scale engineered systems governed by unsteady processes. Specifically, the project will combine time-accurate analysis and...
- 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's "Engineering" program (CFDA 47.041) awarded a $735,872 Project Grant to the University of Pittsburgh to develop a novel digital twin modeling framework for evaluating and minimizing greenhouse gas emissions associated with vertical infrastructure operations. The grant will support research to create high-fidelity 3D digital representations of buildings augmented with real-time sensor data, enabling the analysis of...
- 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 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 $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 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 data for calibration. This aims to significantly reduce computational burden when calibrating digital twins, which are digital representations of complex physical systems. The work will extend integrative optimization frameworks to multi-dimensional, functional, and time-variant calibration problems. It will also incorporate input uncertainty into the optimization to enhance robustness while maintaining tractability. The new methods will be validated through case studies of building energy and wind power systems to fully leverage big data capabilities while addressing challenges posed by data scale and complexity.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $283.1k | 12/20/22 |