Publikation:

Parameter Identification Problems of ODEs with Uncertain Initial Conditions

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2022

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Open Access-Veröffentlichung
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Masterarbeit/Diplomarbeit
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Published

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Zusammenfassung

This thesis gives an insight on gradient descent methods and how they can be applied to solve parameter identification problems pertaining to an ordinary differential equation (ODE) with unknown parameters. Different gradient descent methods such as stochastic gradient descent, projected gradient descent and projected stochastic gradient descent are outlined and their theoretical convergence behaviour is proven. Further, the theoretical foundation is supported by several numerical experiments as the algorithms' practical performances are analysed.

Zusammenfassung in einer weiteren Sprache

Fachgebiet (DDC)
510 Mathematik

Schlagwörter

numerical optimisation, gradient descent, stochastic gradient descent, projected stochastic gradient descent, parameter identification

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ISO 690SAUER, Felix, 2022. Parameter Identification Problems of ODEs with Uncertain Initial Conditions [Master thesis]. Konstanz: Universität Konstanz
BibTex
@mastersthesis{Sauer2022Param-59466,
  year={2022},
  title={Parameter Identification Problems of ODEs with Uncertain Initial Conditions},
  address={Konstanz},
  school={Universität Konstanz},
  author={Sauer, Felix}
}
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Konstanz, Universität Konstanz, Masterarbeit/Diplomarbeit, 2022
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