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Parallel Multiscale Algorithms for Dynamical Systems

Parallel Multiscale Algorithms for Dynamical Systems
动力系统的并行多尺度算法
批准号:
1620396
负责人:
Bjorn Engquist
金额:
$35.0万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2016
资助国家:
美国
项目状态:
已结题
起止时间:
2016-09-01 至 2019-08-31

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项目成果

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中文摘要
翻译
所提出的研究将为未来大规模并行计算机体系结构上的现实计算机模拟开辟重要的问题类。目标区域是具有强烈时间变化的动力学现象。例如,将受益于这种改进的模拟能力的典型过程是化学和生物系统的分子动力学、振动机械系统、系统生物现象、大气流动和地震波传播。新的计算方法和理论理解将在具有挑战性的动力系统的数值解中引入新的范例,具有潜在的许多未来的现实应用。这些领域的学生的培训也是非常重要的,因为他们将塑造学术界和工业界多尺度建模和高性能计算的未来。具有高度振荡解的时间相关系统可以在许多重要的科学和工程领域找到,它们在分析和科学计算方面都提出了巨大的挑战。研究活动的一个主要焦点一直是为这类系统开发多尺度方法,这些方法侧重于这些系统的有效行为,而不是解决所有细节。私人投资促进机构建议制定一个新的框架,以解决未来几年科学计算的一些核心问题。这将从两个方向进行。在一种情况下,早期的技术将被推广到无限维振动系统,并应用于地震波传播。另一个方向是开发下一代大规模并行计算机系统。有效行为的多尺度模型将被用作粗略求解器,以解决具有挑战性的时间并行或准区域计算中的重要障碍。这一结果将促进多尺度建模领域的进一步协同发展。
英文摘要
The proposed research will open important problem classes for realistic computer simulation on future massively parallel computer architectures. The targeted areas are dynamical phenomena with strong variations in time. Typical processes that will benefit from this improved simulation capability are, for example, molecular dynamics for chemical and biological systems, vibrating mechanical systems, systems biological phenomena, atmospheric flow and seismic wave propagation. The novel computational methods and theoretical understanding will introduce new paradigms in the numerical solutions of challenging dynamical systems with a potential for many future realistic applications. The training of students in these fields is also very important, as they will shape the future of the development of multiscale modeling and high performance computing in academia and industry.Time dependent systems that have highly oscillatory solutions can be found in many important fields of science and engineering, and they present great challenges both in analysis and in scientific computation. A major focus of research activities has been on the development of multiscale methods for such systems that focus on the effective behavior of these systems without resolution of all details. The PIs propose to develop a new framework addressing some of the core problems of scientific computing for the coming years. This will be done in two directions. In one the earlier techniques will be generalized to infinite dimensional oscillatory systems with application to seismic wave propagation. The other direction will exploit next generation massively parallel computer systems. The multiscale models for effective behavior will be used as coarse solvers to resolve important obstacles in the challenging parallel-in-time or parareal computation. The results will facilitate further synergistic advancement in the field of multiscale modeling in general.
期刊论文(1)
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会议论文
DOI: 10.1137/18m1186927
发表时间: 2019
期刊: Multiscale Modeling & Simulation
影响因子: 1.6
作者: [Martin, Lindsay, Tsai, Yen-Hsi R.]
通讯作者: Tsai, Yen-Hsi R.
Machine Learning for Effective Computation in Multiscale Hyperbolic Systems
  • 批准号:
    2208504
  • 项目类别:
    Standard Grant
  • 资助金额:
    $45.0万
  • 财政年份:
    2022
  • 负责人:
    Bjorn Engquist
  • 依托单位:
A New Multiscale Framework for Hyperbolic Problems
  • 批准号:
    1913209
  • 项目类别:
    Standard Grant
  • 资助金额:
    $44.49万
  • 财政年份:
    2019
  • 负责人:
    Bjorn Engquist
  • 依托单位:
Multiscale Computations of Time Dependent Highly Oscillatory Systems
  • 批准号:
    1522792
  • 项目类别:
    Standard Grant
  • 资助金额:
    $19.09万
  • 财政年份:
    2015
  • 负责人:
    Bjorn Engquist
  • 依托单位:
Multiscale Computation of Highly Oscillatory Dynamical Systems
  • 批准号:
    1217203
  • 项目类别:
    Standard Grant
  • 资助金额:
    $49.72万
  • 财政年份:
    2012
  • 负责人:
    Bjorn Engquist
  • 依托单位:
海外基金