CDS&E: Data-Driven Modeling and Analyses of Extreme Waves

异常海浪是罕见事件，但它们构成了最大的海上风险之一。这一计算和数据支持科学与工程奖(CDS&amp；E)将用于开发一个统一的、数据驱动的框架和工具，以推进海洋异常海浪的分析和预测。研究结果将为基于风险的土木工程和海洋系统设计提供关键信息，如海上平台、风力涡轮机和船舶。该框架和工具还将有利于对其他复杂系统的基础研究，这些系统产生必须处理的大量数据--从制造过程到灾害规划和应对。研究人员在大数据集中识别罕见事件特征的工作，可能会推动从医疗诊断到机场安检和先进材料开发等多个领域的基础研究。与美国国家海洋和大气管理局(NOAA)和美国海军的合作将有助于确保这项工作将用于加强天气预报和海上安全。新的框架将被整合到一个由NOAA使用的软件包中，并向研究人员、学生和公众提供。学生将参与这项研究，研究结果将被整合到马里兰大学、美国海军学院和工程女性学生的课程材料和推广活动中。研究人员还将为K-12学生提供关于极端海浪现象的科学艺术展示。这项研究将被用来整合统计学习、信号处理和库普曼算子理论，以开发软件工具和过程，这些工具和过程可以配置为识别各种领域的能量局部化--包括海浪--以提供预测能力。作为罕见事件，对异常海浪的测量很少，但有大量关于风暴、其他大气事件和海浪行为的信息。一个跨学科的团队将整合现有的NOAA数据库、通用图形处理单元(GPGPU)计算和机器学习的应用。研究人员将加深我们对如何使用并行处理通过结合GPU和中央处理器(CPU)的处理能力进行大规模计算的理解。通过更有效地使用一套模拟和信号处理工具，如小波、快速傅立叶变换和蒙特卡罗模拟，这一增强的理解将促进动力系统的基础研究。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Rogue (freak) waves are rare events but they pose one of the greatest maritime risks. This Computational and Data-Enabled Science and Engineering (CDS&E) award would be used to develop a unified, data-driven framework and tools to advance the analysis and prediction of oceanic freak waves. The results of the research would provide key information for risk-based design of civil engineering and marine systems such as offshore platforms, wind turbines, and ships. The framework and tools would also benefit fundamental research on other complex systems that generate large amounts of data that must be processed-- ranging from manufacturing processes to disaster planning and response. The researchers' work on identifying signatures of rare events within big data sets could advance fundamental research in a variety of areas from medical diagnostics to airport security and the development of advanced materials. Collaborations with the National Oceanic and Atmospheric Administration (NOAA) and the US Navy will help to ensure that the work will be used to enhance weather forecasting and safety on the seas. The new framework is to be incorporated into a software package, which is used by NOAA, and is to be made available to researchers, students, and the public. Students will participate in the research and the results will be integrated into course materials and outreach activities for students in the University of Maryland, the US Naval Academy, and Women in Engineering. The researchers will also provide art-in-science displays on extreme wave phenomena for K-12 students. This research will be used to integrate statistical learning, signal processing, and Koopman operator theory to develop software tools and processes that can be configured to identify energy localizations across a variety of domains--including ocean waves-- to provide a prediction capability. As rare events, there are few measurements of rogue waves but there is a wealth of information on storms, other atmospheric events and wave behavior. An interdisciplinary team will integrate existing NOAA databases, General-Purpose Graphics Processing Units (GPGPU) computing, and the application of machine learning. The investigators will advance our understanding of how to use parallel processing of large-scale computations done by combining the processing power of GPUs and Central Processing Units (CPUs). This enhanced understanding will advance fundamental research in dynamical systems by enabling more effective use of a suite of simulation and signal processing tools, such as wavelets, Fast Fourier Transforms, and Monte Carlo simulations.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.