CyberTraining: Pilot: Employing Proper Orthogonal Decomposition (POD) and High-Performance Computing (HPC) in Advanced CI

POD（Proper Orthogonal Decomposition）是一种高效的数据驱动学习算法，用于求解多维常/偏微分方程（ODE/PDE）。然而，POD在典型的研究生课程中很少涉及，因此国家在科学和工程研究中没有充分利用这种先进的算法。为了填补这一空白，该项目将为工程和科学相关学科的学员举办为期两周的在线研讨会，并将开发的教学材料纳入现有的高性能计算研究生课程。通过这样做，该项目提供了一个教育生态系统，使科学家和工程师能够进行计算和数据驱动的科学。通过培训不同学科的研究生，博士后和教职员工，该项目将帮助科学工作者为先进的CI研究做好准备，这将有助于提高研究生产力，使研究人员能够有效地解决复杂的社会问题。该项目将通过在开源计算平台上进行密集的综合教学，帮助在关键需求领域发展国家研究队伍，以使用采用HPC的POD模型解决ODE/PDE。培训将包括基于数据驱动POD学习算法的跨学科项目，用于各种科学和工程学科中计算密集型多物理场仿真问题。该研讨会将为学员提供POD和相关主题的强化指导，包括解决ODE/PDE和特征值问题的开源平台。培训在一个研究项目中达到高潮，学员在该项目中学习高级计算工具和HPC技能的实施。该项目将扩大先进的CI的获取和采用，同时将CI技能融入现有的课程模式，并促进跨学科和跨机构的研究合作。该奖项反映了NSF的法定使命，并被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

Proper Orthogonal Decomposition (POD) is a highly effective, data-driven learning algorithm for solving multi-dimensional Ordinary/Partial Differential Equations (ODEs/PDEs). However, POD is rarely covered in the typical graduate curriculum, and thus the nation is not fully leveraging this advanced algorithm in science and engineering research. To fill this void, this project will conduct a two-week online workshop for trainees in engineering and science-related disciplines and will integrate the developed instructional material into an existing graduate course on High-performance Computing (HPC). In doing so, this project provides for an educational ecosystem enabling computational and data-driven science for scientists and engineers. By training a diverse group of graduate students, post-docs, and faculty members in various disciplines, this project will help prepare the scientific workforce for advanced CI-enabled research, which will serve to enhance research productivity and enable researchers to effectively address complex societal problems. The project will help develop the national research workforce in areas of critical need through intensive, integrated instruction on open-source computing platforms to solve ODEs/PDEs by use of POD models that employ HPC. The training will incorporate team-based interdisciplinary projects based on data-driven POD learning algorithm for computationally intensive multiphysics simulation problems in various science and engineering disciplines. The workshop will provide trainees with intensive instruction on POD and related topics, including open source platforms to solve ODEs/PDEs and eigenvalue problems. Training culminates in a research project where trainees learn advanced computational tools and implementation of HPC skills. The project will broaden the access and adoption of advanced CI while integrating CI skills into existing curriculum models and fostering inter-disciplinary and inter-institutional research collaborations.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.