Approximation Bounds for Model Reduction on Polynomially Mapped Manifolds

For projection-based linear-subspace model order reduction (MOR), it is well known that the Kolmogorov <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>n</mml:mi> </mml:math> -width describes the best-possible error for a reduced order model (ROM) of size <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>n</mml:mi> </mml:math> . In this paper, we provide approximation bounds for ROMs on polynomially mapped manifolds. In particular, we show that the approximation bounds depend on the polynomial degree <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>p</mml:mi> </mml:math> of the mapping function as well as on the linear Kolmogorov <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>n</mml:mi> </mml:math> -width for the underlying problem. This results in a Kolmogorov <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>(</mml:mo> <mml:mi>n</mml:mi> <mml:mo>,</mml:mo> <mml:mi>p</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> -width, which describes a lower bound for the best-possible error for a ROM on polynomially mapped manifolds of polynomial degree <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>p</mml:mi> </mml:math> and reduced size <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>n</mml:mi> </mml:math> .

Authors

Publication Details

Journal
Comptes Rendus Mathématique
Published
2024-12-03
DOI
https://doi.org/10.5802/crmath.632
Citations
3
Primary Topic
Model Reduction and Neural Networks
Type
article
Field-Weighted Citation Impact
0.84

Funders

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article

Approximation Bounds for Model Reduction on Polynomially Mapped Manifolds

Silke Glas, Bernard Haasdonk, Patrick Buchfink
3 citations
Comptes Rendus Mathématique
Model Reduction and Neural Networks
0.84
article

Approximation Bounds for Model Reduction on Polynomially Mapped Manifolds

Silke Glas, Bernard Haasdonk, Patrick Buchfink
article en
3 citations

Abstract

For projection-based linear-subspace model order reduction (MOR), it is well known that the Kolmogorov <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>n</mml:mi> </mml:math> -width describes the best-possible error for a reduced order model (ROM) of size <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>n</mml:mi> </mml:math> . In this paper, we provide approximation bounds for ROMs on polynomially mapped manifolds. In particular, we show that the approximation bounds depend on the polynomial degree <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>p</mml:mi> </mml:math> of the mapping function as well as on the linear Kolmogorov <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>n</mml:mi> </mml:math> -width for the underlying problem. This results in a Kolmogorov <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>(</mml:mo> <mml:mi>n</mml:mi> <mml:mo>,</mml:mo> <mml:mi>p</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> -width, which describes a lower bound for the best-possible error for a ROM on polynomially mapped manifolds of polynomial degree <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>p</mml:mi> </mml:math> and reduced size <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>n</mml:mi> </mml:math> .

Comptes Rendus MathématiqueVol. 362(G13)
Deutsche Forschungsgemeinschaft, Singapore Institute of Manufacturing Technology
Openalex Percentile: Top 27%
Model Reduction and Neural Networks
0.84
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