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

合作研究：推进数据到分发管道，实现可扩展的数据一致反演，以量化沿海灾害的不确定性

• 批准号: 2208461
• 负责人: Clinton Dawson
• 金额: $ 17.46万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2208461&HistoricalAwards=false
• 关键词: Collaborative; Research; Advancing; Data; Distribution

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.

沿海危害是对全球公民，工业和政府的持续威胁。美国兴趣特别关注的是风暴潮和洪水从飓风到从墨西哥湾延伸到北大西洋西部的飓风，北极风暴之间的相互作用和影响北美沿海社区的海冰覆盖之间的相互作用，以及从油轮和深水钻探装备等来源的漏油事件传播。因此，在这些沿海危害的建模和模拟中量化不确定性的能力对于做出有关如何最好地准备，减轻和应对此类危害的数据的决策至关重要。研究小组旨在提高最新的数学，统计和计算能力，以解决这些社会重要性的应用。此外，数学，统计和计算研究广泛适用于科学和工程社区的广泛应用。教育影响包括对该领域的本科生和研究生的培训。该项目需要采用多方面的研究方法，建立在严格的措施理论基​​础上，以扩大数据吻合反转（DCI）的应用，该方法是一种识别，量化和减少基于物理计算模型的不确定性（参数）的方法，用于广泛的复杂物理系统。一个方面是对基于深度学习的数据到分布管道的开发和分析，以将时空数据云转换为DCI的非参数分布，这些分布可以在管道中纳入最佳的实验设计标准。另一个方面是开发DCI的可扩展方法，该方法同时解决了由高维特征空间引起的计算问题，以及由于计算昂贵的模型而导致的模拟数据可用性有限。第三个方面是开发DCI的迭代方法，该方法可以在操作环境中部署，以确定数据可用时最可能的关键模型参数。 PI将在DCI和数据之间的公共领域软件中实施算法开发。 PIS将主要利用最先进的高级循环（ADCIRC）模型及其变体来建模沿海危害。该奖项反映了NSF的法定任务，并被认为是通过基金会的智力优点和更广泛影响的审查标准通过评估来通过评估来获得支持的。

项目成果

Shinnecock Inlet Uniform Wind Drag Parameter Scan

Shinnecock 入口均匀风阻参数扫描

• DOI: 10.17603/ds2-bmrf-y737
• 发表时间: 2022
• 期刊: Designsafe-CI
• 作者: Del-Castillo-Negrete, Carlos

Parameter estimation with maximal updated densities

使用最大更新密度的参数估计

• DOI: 10.1016/j.cma.2023.115906
• 发表时间: 2023
• 期刊: Computer Methods in Applied Mechanics and Engineering
• 影响因子: 7.2
• 作者: Pilosov, Michael;del-Castillo-Negrete, Carlos;Yen, Tian Yu;Butler, Troy;Dawson, Clint
• 通讯作者: Dawson, Clint