POD-ROM methods: error analysis for continuous parametrized approximations

Abstract This paper studies the numerical approximation of parametric time-dependent partial differential equations (PDEs) by proper orthogonal decomposition reduced order models (POD-ROMs). Although many papers in the literature consider reduced order models for parametric equations, a complete error analysis of the methods is still a challenge. We introduce and analyse in this paper a new POD method based on finite differences (with respect to time and with respect to the parameters that may be considered). We obtain a priori bounds for the new method valid for any value of time in a given time interval and any value of the parameter in a given parameter interval. Our design of the new POD method allows us to prove pointwise-in-time error estimates as opposed to average error bounds obtained typically in POD methods. Most of the papers concerning POD methods for parametric equations are just based on the snapshots computed at different times and parameter values instead of their difference quotients. We show that the error analysis of the present paper can also cover the error analysis of that case (that we call standard). Some numerical experiments compare our new approach with the standard one and support the error analysis.

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Publication Details

Journal
IMA Journal of Numerical Analysis
Published
2025-12-09
DOI
https://doi.org/10.1093/imanum/draf150
Primary Topic
Model Reduction and Neural Networks
Type
article
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article

POD-ROM methods: error analysis for continuous parametrized approximations

Bosco Garcia-Archilla, Julia Novo, Alicia García-Mascaraque
IMA Journal of Numerical Analysis
Model Reduction and Neural Networks
article

POD-ROM methods: error analysis for continuous parametrized approximations

Bosco Garcia-Archilla, Julia Novo, Alicia García-Mascaraque
article en

Abstract

Abstract This paper studies the numerical approximation of parametric time-dependent partial differential equations (PDEs) by proper orthogonal decomposition reduced order models (POD-ROMs). Although many papers in the literature consider reduced order models for parametric equations, a complete error analysis of the methods is still a challenge. We introduce and analyse in this paper a new POD method based on finite differences (with respect to time and with respect to the parameters that may be considered). We obtain a priori bounds for the new method valid for any value of time in a given time interval and any value of the parameter in a given parameter interval. Our design of the new POD method allows us to prove pointwise-in-time error estimates as opposed to average error bounds obtained typically in POD methods. Most of the papers concerning POD methods for parametric equations are just based on the snapshots computed at different times and parameter values instead of their difference quotients. We show that the error analysis of the present paper can also cover the error analysis of that case (that we call standard). Some numerical experiments compare our new approach with the standard one and support the error analysis.

IMA Journal of Numerical Analysis
Universidad Autónoma de Madrid (ES), Universidad de Sevilla (ES)
Openalex Percentile: Top 24%
Model Reduction and Neural Networks
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