ATD: A model-assisted data-driven framework for prediction of rare extreme events from sparse measurements

ATD：模型辅助数据驱动框架，用于通过稀疏测量预测罕见极端事件

• 批准号: 2220548
• 负责人: Mohammad Farazmand
• 金额: $ 20万
• 依托单位: North Carolina State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-15 至 2026-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2220548&HistoricalAwards=false
• 关键词: ATD; model; assisted; data; driven

Rare extreme events, such as tsunamis, oceanic rogue waves, wildfires, and earthquakes, cause immense human, environmental, and financial damage. Yet, their effective prediction, quantification, and mitigation remains a major challenge. This project develops a synergistic framework for accurate and real-time prediction of rare extreme events using both observational data and mathematical models. The resulting methods will increase the accuracy of predictions based on available observational data. At the same time, they will significantly reduce the computational cost, making real-time predictions feasible. This project will also provide research training opportunities for graduate students.The evolution of spatiotemporal systems, such as fluid flows and waves, is described by partial differential equations (PDEs). High-resolution numerical simulations of these PDE models are valuable since they provide detailed information about the system and its dynamics. However, their high computational cost renders them ineffective for making real-time predictions. More importantly, the PDE models require detailed spatial measurements of the system which are often unattainable in practice where system observations are limited to a relatively small number of sensor locations. The objective of this project is to determine the optimal location of the sensors in order to enable accurate and real-time prediction of extreme events. The framework consists of two phases: (1) First, offline PDE simulations are leveraged to identify the optimal sensing locations and to machine learn a reliable indicator of extreme events. (2) Optimal real-time measurements and the pre-trained machine learning algorithm are used to predict future extreme events. Phase 1 is computationally expensive but is carried out offline and only once. The results are used in phase 2 in order to make fast real-time predictions with minimal computational cost. As such, the final results will increase the accuracy of extreme event prediction while decreasing its computational cost.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.

罕见的极端事件，例如海啸，海洋流氓波浪，野火和地震，造成巨大的人类，环境和经济损害。然而，他们的有效预测，量化和缓解仍然是一个重大挑战。该项目开发了一个协同框架，用于使用观察数据和数学模型对罕见极端事件进行准确和实时预测。最终的方法将根据可用的观察数据提高预测的准确性。同时，它们将大大降低计算成本，从而使实时预测可行。该项目还将为研究生提供研究培训机会。时空系统（例如流体流和波浪）的演变由部分微分方程（PDES）描述。这些PDE模型的高分辨率数值模拟是有价值的，因为它们提供了有关系统及其动态的详细信息。但是，它们的高计算成本使他们无法实时预测无效。更重要的是，PDE模型需要对系统的详细空间测量，而在实践中，这些模型通常是无法实现的，而系统观察仅限于相对较少的传感器位置。该项目的目的是确定传感器的最佳位置，以实现极端事件的准确和实时预测。该框架由两个阶段组成：（1）首先，将离线PDE模拟借用以确定最佳的感应位置，并以机器了解极端事件的可靠指标。 （2）最佳的实时测量和预训练的机器学习算法用于预测未来的极端事件。第1阶段在计算上很昂贵，但仅在离线和一次进行。结果在第2阶段使用，以便以最低的计算成本进行快速的实时预测。因此，最终结果将提高极端事件预测的准确性，同时降低其计算成本。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响评估标准通过评估来获得支持的。