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

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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.