Improved a posteriori error bounds for reduced port-Hamiltonian systems

IF 1.7 3区 数学 Q2 MATHEMATICS, APPLIED
Johannes Rettberg, Dominik Wittwar, Patrick Buchfink, Robin Herkert, Jörg Fehr, Bernard Haasdonk
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引用次数: 0

Abstract

Projection-based model order reduction of dynamical systems usually introduces an error between the high-fidelity model and its counterpart of lower dimension. This unknown error can be bounded by residual-based methods, which are typically known to be highly pessimistic in the sense of largely overestimating the true error. This work applies two improved error bounding techniques, namely (a) a hierarchical error bound and (b) an error bound based on an auxiliary linear problem, to the case of port-Hamiltonian systems. The approaches rely on a secondary approximation of (a) the dynamical system and (b) the error system. In this paper, these methods are adapted to port-Hamiltonian systems. The mathematical relationship between the two methods is discussed both theoretically and numerically. The effectiveness of the described methods is demonstrated using a challenging three-dimensional port-Hamiltonian model of a classical guitar with fluid–structure interaction.

改进的还原端口-哈密尔顿系统后验误差边界
基于投影的动态系统模型阶次缩减通常会在高保真模型和低维模型之间引入误差。基于残差的方法可以对这一未知误差进行约束,但众所周知,这种方法通常非常悲观,会在很大程度上高估真实误差。本研究将两种改进的误差约束技术,即 (a) 层次误差约束和 (b) 基于辅助线性问题的误差约束,应用于端口-哈密尔顿系统的情况。这些方法依赖于 (a) 动力系统和 (b) 误差系统的二次近似。在本文中,这些方法都适用于端口-哈密尔顿系统。本文从理论和数值两方面讨论了这两种方法之间的数学关系。本文使用一个具有挑战性的流固耦合经典吉他的三维端口-哈密尔顿模型,证明了所述方法的有效性。
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来源期刊
CiteScore
3.00
自引率
5.90%
发文量
68
审稿时长
3 months
期刊介绍: Advances in Computational Mathematics publishes high quality, accessible and original articles at the forefront of computational and applied mathematics, with a clear potential for impact across the sciences. The journal emphasizes three core areas: approximation theory and computational geometry; numerical analysis, modelling and simulation; imaging, signal processing and data analysis. This journal welcomes papers that are accessible to a broad audience in the mathematical sciences and that show either an advance in computational methodology or a novel scientific application area, or both. Methods papers should rely on rigorous analysis and/or convincing numerical studies.
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