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Identification of Dynamical Systems:...

Patel, Vivak.

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Identification of Dynamical Systems: Identifiability to Stochastic Optimization.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Identification of Dynamical Systems: Identifiability to Stochastic Optimization./
Author:	Patel, Vivak.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
Description:	240 p.
Notes:	Source: Dissertations Abstracts International, Volume: 80-03, Section: B.
Contained By:	Dissertations Abstracts International80-03B.
Subject:	Applied Mathematics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10831449
ISBN:	9780438370487

Identification of Dynamical Systems: Identifiability to Stochastic Optimization.
Patel, Vivak.

Identification of Dynamical Systems: Identifiability to Stochastic Optimization. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 240 p.

Source: Dissertations Abstracts International, Volume: 80-03, Section: B.

Thesis (Ph.D.)--The University of Chicago, 2018.

This item must not be sold to any third party vendors.

The central theme of this thesis is to understand two deeply related question. When can a differential system model be identifiable from observations? If the model is identifiable, how can we identify it practically? While these questions are by no means new, we study them in a modern context where systems and models are more complex, observations are more frequent, and the stochastic nature of the underlying phenomenon must be considered. Chapter 1 discusses the nuances of these two questions in this modern context. Chapters 2 and 3 delve into first question by refining notions of identifiability and by contributing necessary conditions for identifiability of certain differential equation models. Chapters 4 to 7 delve into the second question from the perspective of designing computable estimators to handle the higher frequency of observations. Chapter 8 also addresses the second question by designing a novel optimization framework to address phenomenon with a stochastic nature.

ISBN: 9780438370487Subjects--Topical Terms:

1669109
Applied Mathematics.

Identification of Dynamical Systems: Identifiability to Stochastic Optimization.
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245 1 0 $a Identification of Dynamical Systems: Identifiability to Stochastic Optimization.
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500 $a Source: Dissertations Abstracts International, Volume: 80-03, Section: B.
500 $a Publisher info.: Dissertation/Thesis.
500 $a Advisor: Anitescu, Mihai.
502 $a Thesis (Ph.D.)--The University of Chicago, 2018.
506 $a This item must not be sold to any third party vendors.
520 $a The central theme of this thesis is to understand two deeply related question. When can a differential system model be identifiable from observations? If the model is identifiable, how can we identify it practically? While these questions are by no means new, we study them in a modern context where systems and models are more complex, observations are more frequent, and the stochastic nature of the underlying phenomenon must be considered. Chapter 1 discusses the nuances of these two questions in this modern context. Chapters 2 and 3 delve into first question by refining notions of identifiability and by contributing necessary conditions for identifiability of certain differential equation models. Chapters 4 to 7 delve into the second question from the perspective of designing computable estimators to handle the higher frequency of observations. Chapter 8 also addresses the second question by designing a novel optimization framework to address phenomenon with a stochastic nature.
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