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Reduced Order Modelling using Dynamic Mode Decomposition and Koopman Spectral Analysis with Deep Learning

Krantz, David LU and Månsson, Olof LU (2020) In Master's Theses in Mathematical Sciences FMNM01 20201
Mathematics (Faculty of Engineering)
Abstract
In the industry simulation models are commonly used in system development. These models can become complicated in order to capture the physical behaviour of the underlying dynamical system. A high-fidelity representation, which can result in long simulation times, is in some settings not strictly required. A method for overall model fidelity reduction is therefore of interest.

In this thesis, two data-driven methods for model order reduction based on modal decomposition of data have been investigated. More specifically, we explore dynamic mode decomposition (DMD) and Koopman spectral analysis with deep learning. We validated this approach by extracting dominant dynamical characteristics from data for reduced order modelling. This was... (More)
In the industry simulation models are commonly used in system development. These models can become complicated in order to capture the physical behaviour of the underlying dynamical system. A high-fidelity representation, which can result in long simulation times, is in some settings not strictly required. A method for overall model fidelity reduction is therefore of interest.

In this thesis, two data-driven methods for model order reduction based on modal decomposition of data have been investigated. More specifically, we explore dynamic mode decomposition (DMD) and Koopman spectral analysis with deep learning. We validated this approach by extracting dominant dynamical characteristics from data for reduced order modelling. This was achieved by applying the methods to both linear and non-linear dynamical systems.

A reduced order model of a large highly non-linear dynamical system with meaningful fidelity was produced using the DMD method. Additionally, this reduced order model can be simulated significantly faster than the original high-fidelity model. The Koopman method was successfully applied to smaller non-linear systems, but did not capture the dynamics of the large highly non-linear system. (Less)
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author
Krantz, David LU and Månsson, Olof LU
supervisor
organization
course
FMNM01 20201
year
type
H2 - Master's Degree (Two Years)
subject
keywords
non-linear dynamical systems, modal decomposition, Koopman operator theory, machine learning, data-driven analysis
publication/series
Master's Theses in Mathematical Sciences
report number
LUTFNA-3048-2020
ISSN
1404-6342
other publication id
2020:E45
language
English
id
9022444
date added to LUP
2020-07-07 14:51:35
date last changed
2020-07-07 14:51:35
@misc{9022444,
  abstract     = {{In the industry simulation models are commonly used in system development. These models can become complicated in order to capture the physical behaviour of the underlying dynamical system. A high-fidelity representation, which can result in long simulation times, is in some settings not strictly required. A method for overall model fidelity reduction is therefore of interest.

In this thesis, two data-driven methods for model order reduction based on modal decomposition of data have been investigated. More specifically, we explore dynamic mode decomposition (DMD) and Koopman spectral analysis with deep learning. We validated this approach by extracting dominant dynamical characteristics from data for reduced order modelling. This was achieved by applying the methods to both linear and non-linear dynamical systems.

A reduced order model of a large highly non-linear dynamical system with meaningful fidelity was produced using the DMD method. Additionally, this reduced order model can be simulated significantly faster than the original high-fidelity model. The Koopman method was successfully applied to smaller non-linear systems, but did not capture the dynamics of the large highly non-linear system.}},
  author       = {{Krantz, David and Månsson, Olof}},
  issn         = {{1404-6342}},
  language     = {{eng}},
  note         = {{Student Paper}},
  series       = {{Master's Theses in Mathematical Sciences}},
  title        = {{Reduced Order Modelling using Dynamic Mode Decomposition and Koopman Spectral Analysis with Deep Learning}},
  year         = {{2020}},
}