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

网络培训：试点：在高级 CI 中采用适当的正交分解 (POD) 和高性能计算 (HPC)

• 批准号: 2118079
• 负责人: Daqing Hou
• 金额: $ 30万
• 依托单位: Clarkson University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2118079&HistoricalAwards=false
• 关键词: CyberTraining; Pilot; Employing; Proper; Orthogonal

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.

真正交分解(POD)是一种高效的数据驱动学习算法，用于求解多维常/偏微分方程组(ODES/PDE)。然而，POD很少在典型的研究生课程中涵盖，因此国家没有在科学和工程研究中充分利用这一先进的算法。为了填补这一空白，该项目将为工程和科学相关学科的受训者举办为期两周的在线讲习班，并将编写的教学材料纳入现有的高性能计算(HPC)研究生课程。通过这样做，该项目为科学家和工程师提供了一个教育生态系统，使计算和数据驱动的科学成为可能。通过培训不同学科的研究生、博士后和教职员工，该项目将帮助为先进的CI支持的研究准备科学队伍，这将有助于提高研究生产率，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、综合的教学，通过使用高性能计算的POD模型来解决ODE/PDE，从而帮助发展关键需求领域的国家研究队伍。培训将包括基于数据驱动POD学习算法的跨学科团队项目，用于各种科学和工程学科中计算密集型多物理模拟问题。研讨会将为学员提供关于POD和相关主题的密集教学，包括解决常微分方程组和特征值问题的开放源码平台。培训在一个研究项目中达到高潮，在该项目中，受训者学习先进的计算工具和实施高性能计算技能。该项目将扩大先进的竞争情报的获取和采用，同时将竞争情报技能整合到现有的课程模式中，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。

项目成果

Quantum element method for multi-dimensional nanostructures enabled by a projection-based learning algorithm

基于投影的学习算法实现多维纳米结构的量子元方法

• DOI: 10.1016/j.sse.2023.108610
• 发表时间: 2023
• 期刊: Solid-State Electronics
• 影响因子: 1.7
• 作者: Veresko, Martin;Cheng, Ming-Cheng
• 通讯作者: Cheng, Ming-Cheng

"Schrödinger Equation Solver Based on Data-Driven Physics-Informed Generic Building Blocks"

“基于数据驱动的物理信息通用构建块的薛定谔方程求解器”

• 发表时间: 2023
• 期刊: 2023.
• 作者: Veresko, Martin;Cheng, Ming-C.
• 通讯作者: Cheng, Ming-C.