CAREER: A Physics-Informed Machine Learning Framework for Surrogate Modeling of Geostructu — NSF Award to Board of Regents, NSHE,
This Faculty Early Career Development (CAREER) award will support research that attempts to advance a new computational framework that integrates high-fidelity physics and machine learning to transform earthquake engineering for geostructural systems. By reducing the high computational costs typically associated with a
| Award title | CAREER: A Physics-Informed Machine Learning Framework for Surrogate Modeling of Geostructu |
|---|---|
| Award ID | 2441870 |
| Awardee | Board of Regents, NSHE, obo University of Nevada, Reno |
| City | RENO |
| State | NV |
| Amount obligated | $607,802 |
| Principal investigator | Elnaz Esmaeilzadeh Seylabi |
| Program | ECI-Engineering for Civil Infr |
| Start date | 08/01/2025 |
| Abstract | This Faculty Early Career Development (CAREER) award will support research that attempts to advance a new computational framework that integrates high-fidelity physics and machine learning to transform earthquake engineering for geostructural systems. By reducing the high computational costs typically associated with accurate numerical models, this research intends to enable rapid and reliable seismic response predictions for geostructural systems. These predictive capabilities are critical for |
| Source | NSF Awards |
$799/mo
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