AF: Small: Interpolatory Methods for Dimension Reduction of Parametric and Nonlinear Dynamical Systems

AF：小：参数和非线性动力系统降维的插值方法

• 批准号: 1017401
• 负责人: Danny Sorensen
• 金额: $ 49.97万
• 依托单位: William Marsh Rice University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-08-01 至 2014-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1017401&HistoricalAwards=false
• 关键词: AF; Small; Interpolatory; Methods; Dimension

Dynamical systems are a principal tool in the modeling and control of physical phenomena as diverse as signal propagation in the neural/nervous system, circuit simulation, weather forecasting, and fluid dynamics. Direct numerical simulation has been one of very few available means for studying the rich complexity of these phenomena, and in many areas of engineering numerical simulation has become essential to the design process. However, the ever increasing demand for improved model fidelity leads inevitably to dynamical systems of extremely large scale and complexity. Simulations based on such systems often impose unmanageable burdens on both human and computational resources, and thus provide the principal motivation for model reduction - creating smaller, cheaper models that closely mimic the behaviors of the original system. Model reduction can thus result in tractable low dimensional systems that are suitable for analysis, simulation, optimization, and computer-aided system design. The primary theme of this research is an empirical data approach combined with interpolation to overcome limitations of standard projection methods for linear problems. The empirical data may be provided by physical experimentation or by direct numerical simulation. Interpolation conditions enter in several ways to greatly decrease the computational complexity of the reduced models. A three orders of magnitude reduction in computation time can be achieved while retaining excellent accuracy. The proposed research will strive to put these techniques on firm mathematical foundations in order to assure accuracy and also to greatly extend the areas of application in order to establish broad applicability of these new approaches.The proposed approaches represent a significant departure from existing methodology. Developing the proposed methods to a greater level of maturity and applicability will be a significant advance in model reduction.

动力系统是建模和控制物理现象的主要工具，如神经系统中的信号传播，电路仿真，天气预报和流体动力学。 直接数值模拟已经成为研究这些现象的丰富复杂性的极少数可用手段之一，并且在许多工程领域，数值模拟已经成为设计过程中必不可少的手段。 然而，不断增长的需求，提高模型的保真度不可避免地导致非常大的规模和复杂的动态系统。 基于此类系统的仿真通常会对人力和计算资源造成难以管理的负担，因此提供了模型简化的主要动机-创建更小，更便宜的模型，以密切模仿原始系统的行为。 因此，模型简化可以导致易于处理的低维系统，适合于分析，仿真，优化和计算机辅助系统设计。本研究的主要主题是一个经验数据的方法结合插值，以克服线性问题的标准投影方法的局限性。 经验数据可以通过物理实验或直接数值模拟来提供。 插值条件以几种方式进入，以大大降低简化模型的计算复杂性。 计算时间可以减少三个数量级，同时保持良好的精度。 拟议的研究将努力把这些技术的坚实的数学基础，以确保准确性，并大大扩展应用领域，以建立广泛的适用性，这些新的approaches.The拟议的方法是一个显着偏离现有的方法。 将所提出的方法发展到更高的成熟度和适用性水平将是模型简化的一个重大进展。