A Rosenbrock framework for tangential interpolation of port-Hamiltonian descriptor systems

IF 1.8 4区 数学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Tim Moser, Boris Lohmann
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引用次数: 0

Abstract

We present a new structure-preserving model order reduction (MOR) framework for large-scale port-Hamiltonian descriptor systems (pH-DAEs). Our method exploits the structural properties of the Rosenbrock system matrix for this system class and utilizes condensed forms which often arise in applications and reveal the solution behaviour of a system. Provided that the original system has such a form, our method produces reduced-order models (ROMs) of minimal dimension, which tangentially interpolate the original model’s transfer function and are guaranteed to be again in pH-DAE form. This allows the ROM to be safely coupled with other dynamical systems when modelling large system networks, which is useful, for instance, in electric circuit simulation.
port- hamilton广义系统切向插值的Rosenbrock框架
提出了一种新的大规模port- hamilton广义系统(pH-DAEs)保结构模型降阶框架。我们的方法利用了这类系统的Rosenbrock系统矩阵的结构性质,并利用了在应用中经常出现的浓缩形式,揭示了系统的解行为。如果原始系统具有这种形式,则我们的方法产生最小维的降阶模型(ROMs),该模型切向插值原始模型的传递函数,并保证再次以pH-DAE形式存在。这使得ROM在建模大型系统网络时可以安全地与其他动态系统耦合,这在电路仿真中非常有用。
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来源期刊
CiteScore
3.80
自引率
5.30%
发文量
7
审稿时长
>12 weeks
期刊介绍: Mathematical and Computer Modelling of Dynamical Systems (MCMDS) publishes high quality international research that presents new ideas and approaches in the derivation, simplification, and validation of models and sub-models of relevance to complex (real-world) dynamical systems. The journal brings together engineers and scientists working in different areas of application and/or theory where researchers can learn about recent developments across engineering, environmental systems, and biotechnology amongst other fields. As MCMDS covers a wide range of application areas, papers aim to be accessible to readers who are not necessarily experts in the specific area of application. MCMDS welcomes original articles on a range of topics including: -methods of modelling and simulation- automation of modelling- qualitative and modular modelling- data-based and learning-based modelling- uncertainties and the effects of modelling errors on system performance- application of modelling to complex real-world systems.
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