Award Abstract # 2208460
Collaborative Research: Advancing the Data-to-Distribution Pipeline for Scalable Data-Consistent Inversion to Quantify Uncertainties in Coastal Hazards

NSF Org: DMS
Division Of Mathematical Sciences
Recipient: UNIVERSITY OF COLORADO AT DENVER
Initial Amendment Date: June 21, 2022
Latest Amendment Date: June 21, 2022
Award Number: 2208460
Award Instrument: Standard Grant
Program Manager: Yuliya Gorb
ygorb@nsf.gov
 (703)292-2113
DMS
 Division Of Mathematical Sciences
MPS
 Directorate for Mathematical and Physical Sciences
Start Date: September 1, 2022
End Date: August 31, 2026 (Estimated)
Total Intended Award Amount: $375,400.00
Total Awarded Amount to Date: $375,400.00
Funds Obligated to Date: FY 2022 = $375,400.00
History of Investigator:
  • Troy Butler (Principal Investigator)
    butler.troy.d@gmail.com
Recipient Sponsored Research Office: University of Colorado at Denver-Downtown Campus
1380 LAWRENCE ST STE 300
DENVER
CO  US  80204-2055
(303)724-0090
Sponsor Congressional District: 01
Primary Place of Performance: University of Colorado at Denver-Downtown Campus
1201 Larimer Street, Suite 4000
Denver
CO  US  80217-3364
Primary Place of Performance
Congressional District:
01
Unique Entity Identifier (UEI): M6CXZ6GSJW84
Parent UEI:
NSF Program(s): COMPUTATIONAL MATHEMATICS,
MSPA-INTERDISCIPLINARY
Primary Program Source: 01002223DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 5294, 1303, 9263, 079Z
Program Element Code(s): 127100, 745400
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.049

ABSTRACT

Coastal hazards are a persistent threat to citizenry, industry, and governments worldwide. Of particular concern to US interests are storm surge and flooding from hurricanes in communities stretching from the Gulf of Mexico to the western North Atlantic, interactions between Arctic storms and evolving sea ice coverage impacting North American coastal communities, and oil spill spread from sources such as tankers and deep-water drilling rigs. The ability to quantify uncertainties in the modeling and simulation of these coastal hazards is therefore critical to making data-informed decisions about how to best prepare, mitigate, and respond to such hazards. The research team aims to advance state-of-the-art mathematical, statistical, and computational capabilities to address these applications of societal importance. Moreover, the mathematical, statistical, and computational research are broadly applicable to a wide range of applications of interest to both the scientific and engineering communities. Educational impacts include the training of undergraduate and graduate students in this field.

This project requires a multi-faceted research approach built upon a rigorous measure-theoretic foundation to expand the application of Data-Consistent Inversion (DCI), a methodology to identify, quantify, and reduce sources of uncertainty for inputs (parameters) of physics-based computational models, to a wide range of complex physical systems. One facet is the development and analysis of a deep learning based data-to-distribution pipeline to transform spatial-temporal data clouds into non-parametric distributions for DCI that can incorporate optimal experimental design criteria within the pipeline. Another facet is the development of a scalable approach to DCI that simultaneously addresses computational issues arising from high-dimensional feature-spaces as well as limited availability of simulated data due to computationally expensive models. A third facet is the development of an iterative approach to DCI that can be deployed in an operational setting to identify the most likely critical model parameters as data become available. The PIs will implement the algorithmic developments in public domain software for DCI and the data-to-distribution pipeline. The PIs will primarily utilize the state-of-the-art Advanced Circulation (ADCIRC) model and its variants for modeling coastal hazards.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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Bingham, Derek and Butler, Troy and Estep, Don "Inverse Problems for Physics-Based Process Models" Annual Review of Statistics and Its Application , v.11 , 2024 https://doi.org/10.1146/annurev-statistics-031017-100108 Citation Details
Janani, Negar and Young, Kendra A and Kinney, Greg and Strand, Matthew and Hokanson, John E and Liu, Yaning and Butler, Troy and Austin, Erin "A novel application of dataconsistent inversion to overcome spurious inference in genomewide association studies" Genetic Epidemiology , 2024 https://doi.org/10.1002/gepi.22563 Citation Details
Pilosov, Michael and del-Castillo-Negrete, Carlos and Yen, Tian Yu and Butler, Troy and Dawson, Clint "Parameter estimation with maximal updated densities" Computer Methods in Applied Mechanics and Engineering , v.407 , 2023 https://doi.org/10.1016/j.cma.2023.115906 Citation Details

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