Sectional Kolmogorov N-widths for parameter-dependent function spaces: a general framework with application to parametrized Friedrichs’ systems

IF 2.6 3区 数学 Q2 MATHEMATICS, APPLIED
Christian Engwer, Mario Ohlberger, Lukas Renelt
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引用次数: 0

Abstract

We investigate parametrized variational problems where for each parameter the solution may originate from a different parameter-dependent function space. Our main motivation is the theory of Friedrichs’ systems, a large abstract class of linear PDE problems whose solutions are sought in operator- (and thus parameter-)dependent graph spaces. Other applications include function spaces on parametrized domains or discretizations involving data-dependent stabilizers. Concerning the set of all parameter-dependent solutions, we argue that in these cases, the interpretation as a “solution manifold” widely adopted in the model order reduction community is no longer applicable. Instead, we propose a novel framework based on the theory of fiber bundles and explain how established concepts such as approximability generalize by introducing a sectional Kolmogorov N-width. Further, we prove exponential approximation rates of this N-width if a norm equivalence criterion is fulfilled. Applying this result to problems with Friedrichs’ structure then gives a sufficient criterion that can be easily verified.

参数相关函数空间的分段Kolmogorov n -宽度:一个应用于参数化friedrich系统的一般框架
我们研究了参数化变分问题,其中每个参数的解可能来自不同的参数相关函数空间。我们的主要动机是弗里德里希系统的理论,这是一个大的抽象类线性PDE问题,其解是在算子(因此是参数)相关的图空间中寻求的。其他应用包括参数化域上的函数空间或涉及数据相关稳定器的离散化。对于所有参数相关解的集合,我们认为在这些情况下,模型降阶界广泛采用的“解流形”解释不再适用。相反,我们提出了一个基于纤维束理论的新框架,并通过引入截面Kolmogorov n -宽度来解释诸如近似性之类的既定概念如何推广。进一步,在满足范数等价准则的情况下,证明了该n -宽度的指数逼近率。将这一结果应用于Friedrichs结构的问题,给出了一个容易验证的充分判据。
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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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