This three-year, $349,200 National Science Foundation project grant supports research to advance mathematical and computational modeling capabilities for quantifying uncertainties in coastal hazard simulations. Funded under the Mathematical and Physical Sciences program (CFDA 47.049), the University of Texas at Austin will lead efforts to develop and apply a data-to-distribution pipeline using deep learning techniques, scalable data-consistent inversion approaches, and iterative methods for operational deployment. These algorithmic advancements will help identify and reduce uncertainties in model parameters for physics-based simulations of hurricanes, Arctic storms, and oil spills. The researchers will implement their developments in publicly available software for data-consistent inversion and distribution modeling. Modeling efforts will primarily utilize the ADCIRC coastal hazards model. Results from this research aim to better inform decisions around preparing for, mitigating, and responding to coastal threats.
Mod # | Description | Reason For Modification | Federal Obligation (Click to sort descending) | Date (Click to sort ascending) |
|---|---|---|---|---|
| Not listed | $174.6k | 6/21/22 |