On the construction of POD models from partial observations

P. Astrid; S. Weiland

doi:10.1109/CDC.2005.1582500

On the construction of POD models from partial observations

P. Astrid, S. Weiland

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

8 Citations (Scopus)

Abstract

This paper discusses the use of partial state observations in the construction of reduced order models based on proper orthogonal decompositions (POD). A main motivation for this work lies in the observation that reductions of the state dimension of large scale nonlinear and time-varying models hardly enhances the computational speed of these models. It is shown that information from output variables or sampled state information can be used in an efficient manner to accelerate computation speed in reduced order models while allowing state recovery properties in an exact or approximate sense.

Original language	English
Title of host publication	44th IEEE Conference on Decision and Control, Seville, Spain
Place of Publication	Piscataway
Publisher	Institute of Electrical and Electronics Engineers
Pages	2272-2277
Number of pages	6
ISBN (Print)	0-7803-9567-0
DOIs	https://doi.org/10.1109/CDC.2005.1582500
Publication status	Published - 2005
Event	44th IEEE Conference on Decision and Control and European Control Conference (CDC-ECC 2005) - "Melia Sevilla Hotel", Seville, Spain Duration: 12 Dec 2005 → 15 Dec 2005 Conference number: 44

Conference

Conference	44th IEEE Conference on Decision and Control and European Control Conference (CDC-ECC 2005)
Abbreviated title	CDC-ECC 2005
Country/Territory	Spain
City	Seville
Period	12/12/05 → 15/12/05

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10.1109/CDC.2005.1582500

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@inproceedings{b3597e00c8f24adea2ddf53d79ccef6e,

title = "On the construction of POD models from partial observations",

abstract = "This paper discusses the use of partial state observations in the construction of reduced order models based on proper orthogonal decompositions (POD). A main motivation for this work lies in the observation that reductions of the state dimension of large scale nonlinear and time-varying models hardly enhances the computational speed of these models. It is shown that information from output variables or sampled state information can be used in an efficient manner to accelerate computation speed in reduced order models while allowing state recovery properties in an exact or approximate sense.",

author = "P. Astrid and S. Weiland",

year = "2005",

doi = "10.1109/CDC.2005.1582500",

language = "English",

isbn = "0-7803-9567-0",

pages = "2272--2277",

booktitle = "44th IEEE Conference on Decision and Control, Seville, Spain",

publisher = "Institute of Electrical and Electronics Engineers",

address = "United States",

note = "44th IEEE Conference on Decision and Control and European Control Conference (CDC-ECC 2005), CDC-ECC 2005 ; Conference date: 12-12-2005 Through 15-12-2005",

}

Astrid, P & Weiland, S 2005, On the construction of POD models from partial observations. in 44th IEEE Conference on Decision and Control, Seville, Spain., TuA05.5, Institute of Electrical and Electronics Engineers, Piscataway, pp. 2272-2277, 44th IEEE Conference on Decision and Control and European Control Conference (CDC-ECC 2005), Seville, Spain, 12/12/05. https://doi.org/10.1109/CDC.2005.1582500

TY - GEN

T1 - On the construction of POD models from partial observations

AU - Astrid, P.

AU - Weiland, S.

N1 - Conference code: 44

PY - 2005

Y1 - 2005

N2 - This paper discusses the use of partial state observations in the construction of reduced order models based on proper orthogonal decompositions (POD). A main motivation for this work lies in the observation that reductions of the state dimension of large scale nonlinear and time-varying models hardly enhances the computational speed of these models. It is shown that information from output variables or sampled state information can be used in an efficient manner to accelerate computation speed in reduced order models while allowing state recovery properties in an exact or approximate sense.

AB - This paper discusses the use of partial state observations in the construction of reduced order models based on proper orthogonal decompositions (POD). A main motivation for this work lies in the observation that reductions of the state dimension of large scale nonlinear and time-varying models hardly enhances the computational speed of these models. It is shown that information from output variables or sampled state information can be used in an efficient manner to accelerate computation speed in reduced order models while allowing state recovery properties in an exact or approximate sense.

U2 - 10.1109/CDC.2005.1582500

DO - 10.1109/CDC.2005.1582500

M3 - Conference contribution

SN - 0-7803-9567-0

SP - 2272

EP - 2277

BT - 44th IEEE Conference on Decision and Control, Seville, Spain

PB - Institute of Electrical and Electronics Engineers

CY - Piscataway

T2 - 44th IEEE Conference on Decision and Control and European Control Conference (CDC-ECC 2005)

Y2 - 12 December 2005 through 15 December 2005

ER -

On the construction of POD models from partial observations

Abstract

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