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

基本信息

  • 批准号:
    0636336
  • 负责人:
  • 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
  • 起止时间:
    2006-10-01 至 2010-03-31
  • 项目状态:
    已结题

项目摘要

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.
PI:乔治·卡尼亚达基斯(布朗大学),Steven Dong(普渡大学)和尼古拉斯·卡罗尼斯(北方伊利诺伊大学)编号:CI-TEAM提案0636336标题:CI-TEAM实施项目:合作研究:培训高级网络基础设施工具和概念的仿真科学家项目摘要:利用TeraGrid(TG)及其未来扩展所集成的国家最强大的开放计算资源,将在仿真科学和生命科学中实现其他不可行的发现,特别是,也提供了一个前所未有的机会,与我们国家的学生和教育工作者分享这一独特的科学成就。我们的演示有效地使用TG资源在执行第一个跨站点的模拟和可视化的人体动脉树创造了巨大的热情教师和他们的学生谁有兴趣访问这个新的模拟环境。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这项工作。我们的目标是降低使用TG模拟的障碍,我们的目标是:(1)动员生物力学研究社区,(2)培养新一代的模拟科学家,(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将许多大型生物学努力整合到一个连贯和令人兴奋的未来研究方向以及开发有效的新训练方法的共同线索。 更广泛的影响:在这个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过渡到网格计算的TG具有潜在的无限的可扩展性。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,以便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更好地为未来几代科学家、工程师和教育工作者使用和开发网络基础设施做好准备。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,特别是大学生参与跨学科项目,以及高中生,特别是妇女和少数民族学生参与的外联活动。

项目成果

期刊论文数量(0)
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会议论文数量(0)
专利数量(0)

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George Karniadakis其他文献

Correction to: A computational mechanics special issue on: data-driven modeling and simulation—theory, methods, and applications
  • DOI:
    10.1007/s00466-019-01747-7
  • 发表时间:
    2019-06-28
  • 期刊:
  • 影响因子:
    3.800
  • 作者:
    Wing Kam Liu;George Karniadakis;Shaoqiang Tang;Julien Yvonnet
  • 通讯作者:
    Julien Yvonnet
Physics-Informed Learning Machines for Partial Differential Equations: Gaussian Processes Versus Neural Networks
用于偏微分方程的物理学习机:高斯过程与神经网络
En-DeepONet: An enrichment approach for enhancing the expressivity of neural operators with applications to seismology
  • DOI:
    10.1016/j.cma.2023.116681
  • 发表时间:
    2024-02-15
  • 期刊:
  • 影响因子:
  • 作者:
    Ehsan Haghighat;Umair bin Waheed;George Karniadakis
  • 通讯作者:
    George Karniadakis
Simulating and visualizing the human arterial system on the TeraGrid
  • DOI:
    10.1016/j.future.2006.03.019
  • 发表时间:
    2006-10-01
  • 期刊:
  • 影响因子:
  • 作者:
    Suchuan Dong;Joseph Insley;Nicholas T. Karonis;Michael E. Papka;Justin Binns;George Karniadakis
  • 通讯作者:
    George Karniadakis
CMINNs: Compartment model informed neural networks — Unlocking drug dynamics
  • DOI:
    10.1016/j.compbiomed.2024.109392
  • 发表时间:
    2025-01-01
  • 期刊:
  • 影响因子:
  • 作者:
    Nazanin Ahmadi Daryakenari;Shupeng Wang;George Karniadakis
  • 通讯作者:
    George Karniadakis

George Karniadakis的其他文献

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{{ truncateString('George Karniadakis', 18)}}的其他基金

Collaborative Research: AMPS: Multi-Fidelity Modeling via Machine Learning for Real-time Prediction of Power System Behavior
合作研究:AMPS:通过机器学习进行多保真度建模,实时预测电力系统行为
  • 批准号:
    1736088
  • 财政年份:
    2017
  • 资助金额:
    $ 38.44万
  • 项目类别:
    Continuing Grant
MANNA 2017: Modeling, Analysis, and Numerics for Nonlocal Applications
MANNA 2017:非局部应用的建模、分析和数值
  • 批准号:
    1747867
  • 财政年份:
    2017
  • 资助金额:
    $ 38.44万
  • 项目类别:
    Standard Grant
New evolution equations of the joint response-excitation PDF for stochastic modeling: Theory and numerical methods
用于随机建模的联合响应激励 PDF 的新演化方程:理论和数值方法
  • 批准号:
    1216437
  • 财政年份:
    2012
  • 资助金额:
    $ 38.44万
  • 项目类别:
    Continuing Grant
Collaborative Research: Scalable Multiscale Models for the Cerebrovasculature: Algorithms, Software and Petaflop Simulations
合作研究:可扩展的脑血管多尺度模型:算法、软件和千万亿次模拟
  • 批准号:
    0904288
  • 财政年份:
    2009
  • 资助金额:
    $ 38.44万
  • 项目类别:
    Standard Grant
Multiscale Modeling of Flow over Functionalized Surfaces: Algorithms and Applications
功能化表面流动的多尺度建模:算法和应用
  • 批准号:
    0852948
  • 财政年份:
    2009
  • 资助金额:
    $ 38.44万
  • 项目类别:
    Standard Grant
Overcoming the Bottlenecks in Polynomial Chaos: Algorithms and Applications to Systems Biology and Fluid Mechanics
克服多项式混沌的瓶颈:系统生物学和流体力学的算法和应用
  • 批准号:
    0915077
  • 财政年份:
    2009
  • 资助金额:
    $ 38.44万
  • 项目类别:
    Standard Grant
Multiscale Models and Petaflops Simulations on the Human Brain Vascular Network
人脑血管网络的多尺度模型和千万亿次模拟
  • 批准号:
    0845449
  • 财政年份:
    2008
  • 资助金额:
    $ 38.44万
  • 项目类别:
    Standard Grant
International Conference on Spectral and High-Order Methods 2009 - ICOSAHOM'09; June 2009, Trondheim, Norway
2009 年光谱和高阶方法国际会议 - ICOSAHOM09;
  • 批准号:
    0839866
  • 财政年份:
    2008
  • 资助金额:
    $ 38.44万
  • 项目类别:
    Standard Grant
AMC-SS: A Multi-Element Generalized Polynomial Chaos Method for Modeling Uncertainty in Flow Simulations
AMC-SS:一种用于流体仿真中不确定性建模的多元素广义多项式混沌方法
  • 批准号:
    0510799
  • 财政年份:
    2005
  • 资助金额:
    $ 38.44万
  • 项目类别:
    Standard Grant
A Stochastic Molecular Dynamics Method for Multiscale Modeling of Blood Platlet Pheonmena
血小板现象多尺度建模的随机分子动力学方法
  • 批准号:
    0506312
  • 财政年份:
    2005
  • 资助金额:
    $ 38.44万
  • 项目类别:
    Continuing Grant

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