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Advanced Projection Techniques for Dimension Reduction of Large Scale Dynamical Systems

Advanced Projection Techniques for Dimension Reduction of Large Scale Dynamical Systems
用于大规模动力系统降维的先进投影技术
批准号:
0634902
负责人:
Danny Sorensen
金额:
$25.0万
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 2010-09-30

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中文摘要
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英文摘要
Model reduction seeks to replace a large-scale system of differential equations by a system of substantially lower dimension that has response characteristics similar to the original system while requiring far less computational resources. Such large-scale systems often arise through spatial discretization of time dependent PDE systems. For example, in chip manufacturing the physical verification step involves detailed simulation of all constituent components of the chip to verify its behavior. Full simulation is intractable due to computational complexity. A reduced model with guaranteed accuracy is required to complete a reliable simulation in a reasonable period of time. Additional applications of broad impact include: weather prediction, air quality management, micro-electro-mechanical systems, and many others.In general terms, this research is focused on the development, analysis, and implementation of reduction methods for very large problems. Where needed, the work will involve extending the underlying theory of dimension reduction. The primary goal is to provide reliable and efficient dimension reduction methods that preserve structure and system properties with bounds on the approximation error. More specifically, this project is concerned with the investigation of four topics in model reduction. (a) Selection of interpolation points in rational Krylov methods with new applications in reduction methods which (i) preserve dissipativity, and (ii) are optimal in the H2 norm. (b) Provably convergent parameter free large scale Lyapunov solvers with applications to approximate balanced truncation with rigorous error bounds.(c) Symmetry preserving principal component analysis for dimension reduction based upon a new symmetry preserving singular value decomposition; and (d) domain decomposition techniques for rapid solution and dimension reduction of large scale systems with application to power grids of VLSI chips and complex building models.
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AF:Small: Data-Driven Dimension Reduction of Linear and Nonlinear Systems
  • 批准号:
    1320866
  • 项目类别:
    Standard Grant
  • 资助金额:
    $40.0万
  • 财政年份:
    2013
  • 负责人:
    Danny Sorensen
  • 依托单位:
AF: Small: Interpolatory Methods for Dimension Reduction of Parametric and Nonlinear Dynamical Systems
  • 批准号:
    1017401
  • 项目类别:
    Standard Grant
  • 资助金额:
    $49.97万
  • 财政年份:
    2010
  • 负责人:
    Danny Sorensen
  • 依托单位:
Collaborative Research: Numerical Methods for Fully and Implicitly Nonlinear Equations
  • 批准号:
    0914021
  • 项目类别:
    Standard Grant
  • 资助金额:
    $12.0万
  • 财政年份:
    2009
  • 负责人:
    Danny Sorensen
  • 依托单位:
Model Reduction for Structured Dynamical Systems
  • 批准号:
    0306503
  • 项目类别:
    Standard Grant
  • 资助金额:
    $43.66万
  • 财政年份:
    2003
  • 负责人:
    Danny Sorensen
  • 依托单位:
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