A unified framework for the analysis, numerical approximation and model reduction of linear operator equations, Part I: Well-posedness in space and time

We present a unified framework to construct well-posed formulations for large classes of linear operator equations including elliptic, parabolic and hyperbolic partial differential equations. This general approach incorporates known weak variational formulations as well as novel space-time variational forms of the hyperbolic wave equation. The main concept is completion and extension of operators starting from the strong form of the problem. This paper lays the theoretical foundation for a unified approach towards numerical approximation methods and also model reduction of parameterized linear operator equations which will be the subject of the following parts.

Publication Details

Journal
ArXiv.org
Published
2025-08-07
Primary Topic
Model Reduction and Neural Networks
Type
article
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article

A unified framework for the analysis, numerical approximation and model reduction of linear operator equations, Part I: Well-posedness in space and time

ArXiv.org
Model Reduction and Neural Networks
article

A unified framework for the analysis, numerical approximation and model reduction of linear operator equations, Part I: Well-posedness in space and time

article en

Abstract

We present a unified framework to construct well-posed formulations for large classes of linear operator equations including elliptic, parabolic and hyperbolic partial differential equations. This general approach incorporates known weak variational formulations as well as novel space-time variational forms of the hyperbolic wave equation. The main concept is completion and extension of operators starting from the strong form of the problem. This paper lays the theoretical foundation for a unified approach towards numerical approximation methods and also model reduction of parameterized linear operator equations which will be the subject of the following parts.

ArXiv.org
Openalex Percentile: Top 52%
Model Reduction and Neural Networks
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A unified framework for the analysis, numerical approximation and model reduction of linear operator equations, Part I: Well-posedness in space and time · ArXiv.org (2025) | TGRS Research Map | TGRS