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README.md
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title: Master Thesis
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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---
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title: Master Thesis
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emoji: π
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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## Overview
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This is the web verison of my master thesis - the title of the master thesis is: Flow predictions using control-oriented cluster-based network modeling.
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The web version of the master thesis, available at [https://jav-ed.github.io/master_Thesis/](https://jav-ed.github.io/master_Thesis/) and is aimed to offer an interactive reading experience.
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<br/>
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<br/>
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<p align="center">
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<img src="https://jav-ed.github.io/master_Thesis/Data/6_Html_Data/1_Logo_Img/1_Tornado.svg" />
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</p>
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<br/>
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<br/>
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type: Master thesis - TU Braunschweig (Germany)\newline
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title: Flow predictions using control-oriented cluster-based network modeling\newline
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access: https://jav-ed.github.io/master_Thesis/ π
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The used license is: [](https://firstdonoharm.dev/version/3/0/bds-cl-eco-extr-media-mil-sv-xuar.html)
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## Abstract
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Note, to see the content without \gls{var}, please have a look at [https://jav-ed.github.io/master_Thesis/Data/1_Writing/0_Deco/2_1_Abstract.html](https://jav-ed.github.io/master_Thesis/Data/1_Writing/0_Deco/2_1_Abstract.html).
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In this master thesis, a data-driven modeling technique is proposed.
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It enables making predictions for general dynamic systems for unknown model parameter values or operating conditions.
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The tool is denoted as \gls{cnmc}.
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The most recent developed version delivered promising results for the chaotic Lorenz system [@lorenz1963deterministic].
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Since, the earlier work was restricted to the application of only one dynamical system, with this contribution the first major improvement was to allow \gls{cnmc} to be utilized for any general dynamical system.
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For this, \gls{cnmc} was written from scratch in a modular manner.
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The limitation of the number of the dimension and the shape of the trajectory of the dynamical systems are removed.
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Adding a new dynamic system was designed such that it should be as straightforward as possible.
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To affirm this point, 10 dynamic systems, most of which are chaotic systems, are included by default.
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To be able to run \gls{cnmc} on arbitrary dynamic systems in an automated way, a parameter study for the modal decomposition method \gls{nmf} was implemented.
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However, since a single \gls{nmf} solution took up to hours, a second option was added, i.e., \gls{svd}.
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With \gls{svd} the most time-consuming task could be brought to a level of seconds.
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The improvements introduced, allow \gls{cnmc} to be executed on a general dynamic system on a normal computer in a reasonable time.
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Furthermore, \gls{cnmc} comes with its integrated post-processor in form of HTML files to inspect the generated plots in detail.
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All the parameters used in \gls{cnmc} some additional beneficial features can be controlled via one settings file.
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