Sampling-free model reduction of systems with low-rank parameterization
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Sampling-free model reduction of systems with low-rank parameterization Christopher Beattie1 · Serkan Gugercin1 · Zoran Tomljanovi´c2 Received: 24 December 2019 / Accepted: 30 October 2020 / © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract We consider the reduction of parametric families of linear dynamical systems having an affine parameter dependence that allow for low-rank variation in the state matrix. Usual approaches for parametric model reduction typically involve exploring the parameter space to identify representative parameter values and the associated models become the principal focus of model reduction methodology. These models are then combined in various ways in order to interpolate the response. The initial exploration of the parameter space can be a forbiddingly expensive task. A different approach is proposed here that requires neither parameter sampling nor parameter space exploration. Instead, we represent the system response function as a composition of four subsystem response functions that are nonparametric with a purely parameter-dependent function. One may apply any one of a number of standard (non-parametric) model reduction strategies to reduce the subsystems independently, and then conjoin these reduced models with the underlying parameterization to obtain the overall parameterized response. Our approach has elements in common with the parameter mapping approach of Baur et al. (PAMM 14(1), 19–22 2014) but offers greater flexibility and potentially greater control over accuracy. In particular, a data-driven variation of our approach is described that exercises this flexibility through the use of limited frequency-sampling of the underlying non-parametric models. The parametric structure of our system representation allows for a priori guarantees of system stability in the resulting reduced models across the full range of parameter values. Incorporation of system theoretic error bounds allows us to determine appropriate approximation orders for the non-parametric systems sufficient to yield uniformly high accuracy across the parameter range. We illustrate our approach on a class of structural damping optimization problems and on a benchmark model of thermal conduction in a
Communicated by: Stefan Volkwein Zoran Tomljanovi´c
[email protected]
Extended author information available on the last page of the article.
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(2020) 46:83
Adv Comput Math
semiconductor chip. The parametric structure of our reduced system representation lends itself very well to the development of optimization strategies making use of efficient cost function surrogates. We discuss this in some detail for damping parameter and location optimization for vibrating structures. Keywords Sampling-free · Model reduction · Damping optimization Mathematics subject classification (2010) 93C05 · 49J15 · 70Q05 · 70H33
1 Introduction Consider a linear time invariant dynamical system, parameterized with a k T dimensional parameter vector p = p1 , p2 , . . . , pk ∈ Ω ⊆ Rk and rep
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