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CI-TEAM Implementation Project: Collaborative Research: Training Simulation Scientists in Advanced Cyberinfrastructure Tools and Concepts

CI-TEAM Implementation Project: Collaborative Research: Training Simulation Scientists in Advanced Cyberinfrastructure Tools and Concepts
CI-TEAM 实施项目:协作研究:培训模拟科学家掌握先进的网络基础设施工具和概念
批准号:
0636336
负责人:
George Karniadakis
金额:
$38.44万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2006
资助国家:
美国
项目状态:
已结题
起止时间:
2006-10-01 至 2010-03-31

项目摘要

项目成果

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中文摘要
翻译
PIS:George Karniadakis(布朗大学),Steven Dong(普渡大学)和Nicholas Karonis(北伊利诺伊大学)编号:CI-Team Proposal 0636336标题:CI-Team Implementation Project:Collaborative Research:培训模拟科学家先进的网络基础设施工具和概念项目摘要:利用TeraGrid(TG)整合的美国最强大的开放计算资源及其未来的扩展将使我们能够在模拟科学,特别是在生命科学方面做出原本不可能的发现,并提供一个前所未有的机会,与我国的学生和教育工作者分享这一独特的科学成就。我们的演示有效地利用TG资源对人体动脉树进行了首次跨站点模拟和可视化,这在对访问这一新的模拟环境感兴趣的教师和他们的学生中产生了极大的热情。作为当今计算科学中一些最先进工具的开发者和用户,我们希望将这些工具提供给更广泛的科学界,培训培训人员,并让博士后、研究生和本科生以及高中生和他们的老师参与到这一努力中来。我们的目标是降低TG模拟的使用门槛,我们的目标是:(1)动员生物力学研究社区,(2)培养新一代模拟科学家,(3)激励年轻学生成为跨学科模拟科学的未来领导者。为此,我们将在TG上实现一个生物力学网关,并将其作为进一步开发和生物医学研究的主要平台和仿真框架。我们为人类动脉树开发的框架可以作为共同的主线,将一些大型生物努力整合到一个连贯和令人兴奋的未来研究方向,并开发有效的新训练方法。更广泛的影响:该项目中实现的软件将是开源的,并将分发给所有TG用户,促进计算生物学从传统计算到TG上的网格计算的过渡,具有潜在的无限可扩展性。新的模拟环境将是教育和培训新一代跨学科科学家的关键,使他们能够熟练地在许多学科中使用先进的软件工具和概念。该项目开发的关于网格计算、多尺度生物建模和沉浸式交互环境中的科学可视化的新课程将使未来一代科学家、工程师和教育工作者在使用和开发网络基础设施方面做好更好的准备。我们将与芝加哥地区的社区大学合作,培训他们的教师。该项目还促进不同群体的学生,特别是本科生参与跨学科项目,并促进高中生,特别是妇女和少数群体参与外联活动。
英文摘要
PIs: George Karniadakis (Brown University), Steven Dong (Purdue University) andNicholas Karonis (Northern Illinois University)Number: CI-TEAM Proposal 0636336Title: CI-TEAM Implementation Project: Collaborative Research: Training Simulation Scientists in Advanced Cyberinfrastructure Tools and ConceptsProject Abstract:Harnessing the most powerful open computational resources of the nation as integrated by the TeraGrid (TG) and its future extensions will enable otherwise infeasible discoveries in simulation science, in general, and in life sciences, in particular, as well as presenting an unprecedented opportunity to share this unique scientific achievement with our nation's students and educators. Our demonstration of using effectively TG resources in performing the first cross-site simulations and visualizations of the human arterial tree has created great enthusiasm among faculty and their students who are interested in accessing this new simulation environment. As developers and users of some of the most advanced tools in computational science today, we want to make these tools available to the wider scientific community, to train the trainers, and to engage postdocs, graduate and undergraduate students as well as high school students and their teachers in this effort. Our goal is to lower barriers to the use of TG simulations and our objectives are to: (1) mobilize the biomechanics research community, (2) train a new generation of simulation scientists, and (3) inspire young students to become tomorrow's leaders in inter-disciplinary simulation science. To this end, we will implement a biomechanics gateway on the TG and establish it as the main platform and simulation framework for further developments and biomedical research. The framework we have developed for the human arterial tree can serve as the common thread in integrating a number of large biological endeavors into a coherent and exciting future research direction and in developing effective new training methods. Broader Impact: The software implemented in this project will be open source and will be distributed to all TG users, facilitating a transition in computational biology from traditional computing to grid computing on the TG with potentially unlimited scalability. The new simulation environment will be critical in educating and training a new generation of inter-disciplinary scientists to be comfortable in using advanced software tools and concepts across many disciplines. New courses developed in this project, on grid computing, multiscale biological modeling, and scientific visualization in immersive interactive environments will better prepare future generations of scientists and engineers and educators in the use and development of cyberinfrastructure. We will work with community colleges in the Chicago area to train their faculty. This project also promotes engaging students, especially undergraduates, of diverse groups in inter-disciplinary projects, and in outreach activities engaging high school students, especially women and minorities.
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Collaborative Research: AMPS: Multi-Fidelity Modeling via Machine Learning for Real-time Prediction of Power System Behavior
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    $67.82万
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国内基金
海外基金
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