CAREER: Bayesian Machine-Infused Physics-Based Data Assimilation for Digital Twinning and — NSF Award to Board of Regents, NSHE, o
This Faculty Early Career Development (CAREER) award supports research that enables a new paradigm for the integration of data with machine-infused physics-based computational models to develop digital twins of dynamical systems, thereby promoting the progress of science, and advancing prosperity and welfare. The adven
| Award title | CAREER: Bayesian Machine-Infused Physics-Based Data Assimilation for Digital Twinning and |
|---|---|
| Award ID | 2339251 |
| Awardee | Board of Regents, NSHE, obo University of Nevada, Reno |
| City | RENO |
| State | NV |
| Amount obligated | $599,725 |
| Principal investigator | Hamed Ebrahimian |
| Program | EPSCoR Co-Funding, Dynamics, Control and System D |
| Start date | 09/01/2024 |
| Abstract | This Faculty Early Career Development (CAREER) award supports research that enables a new paradigm for the integration of data with machine-infused physics-based computational models to develop digital twins of dynamical systems, thereby promoting the progress of science, and advancing prosperity and welfare. The advent of the big data era necessitates robust, efficient, and scalable scientific tools that assimilate raw measurements into computational models to enhance the quality and resolution |
| Source | NSF Awards |
$799/mo
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