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Low-Dimensional Data-Driven Models for Forecasting and Control of Chaotic Dynamical Systems.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Low-Dimensional Data-Driven Models for Forecasting and Control of Chaotic Dynamical Systems./
Author:	Linot, Alec J.
Description:	1 online resource (245 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 84-08, Section: B.
Contained By:	Dissertations Abstracts International84-08B.
Subject:	Fluid mechanics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30250557click for full text (PQDT)
ISBN:	9798374410815

Low-Dimensional Data-Driven Models for Forecasting and Control of Chaotic Dynamical Systems.
Linot, Alec J.

Low-Dimensional Data-Driven Models for Forecasting and Control of Chaotic Dynamical Systems. - 1 online resource (245 pages)

Source: Dissertations Abstracts International, Volume: 84-08, Section: B.

Thesis (Ph.D.)--The University of Wisconsin - Madison, 2023.

Includes bibliographical references

Modeling high-dimensional and chaotic dynamics remains a challenging problem with a wide range of applications from controlling turbulent flows, to weather forecasting, to predicting cardiac arrhythmias - to name a few. Two major challenge in modeling these systems is that sometimes the equations are unknown and when they are known solving them can be prohibitively expensive. Due to these issues, only recently have experimental databases become mature enough and computational resources fast enough for there to exist large datasets of high-dimensional chaotic dynamical systems. The existence of these large datasets and advances in machine learning techniques opens the possibility for drastic improvements in the modeling and interpretability of chaotic dynamical systems through data-driven low-dimensional models. Here, we generate extremely low-dimensional "exact" models of chaotic dynamics in dissipative systems.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798374410815Subjects--Topical Terms:

528155
Fluid mechanics.
Subjects--Index Terms:

Chaotic dynamical systemsIndex Terms--Genre/Form:

542853
Electronic books.

Low-Dimensional Data-Driven Models for Forecasting and Control of Chaotic Dynamical Systems.
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245 1 0 $a Low-Dimensional Data-Driven Models for Forecasting and Control of Chaotic Dynamical Systems.
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500 $a Source: Dissertations Abstracts International, Volume: 84-08, Section: B.
500 $a Advisor: Graham, Michael D.
502 $a Thesis (Ph.D.)--The University of Wisconsin - Madison, 2023.
504 $a Includes bibliographical references
520 $a Modeling high-dimensional and chaotic dynamics remains a challenging problem with a wide range of applications from controlling turbulent flows, to weather forecasting, to predicting cardiac arrhythmias - to name a few. Two major challenge in modeling these systems is that sometimes the equations are unknown and when they are known solving them can be prohibitively expensive. Due to these issues, only recently have experimental databases become mature enough and computational resources fast enough for there to exist large datasets of high-dimensional chaotic dynamical systems. The existence of these large datasets and advances in machine learning techniques opens the possibility for drastic improvements in the modeling and interpretability of chaotic dynamical systems through data-driven low-dimensional models. Here, we generate extremely low-dimensional "exact" models of chaotic dynamics in dissipative systems.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2023
538 $a Mode of access: World Wide Web
650 4 $a Fluid mechanics. $3 528155
650 4 $a Computational physics. $3 3343998
650 4 $a Applied mathematics. $3 2122814
653 $a Chaotic dynamical systems
653 $a Forecasting
653 $a Cardiac arrhythmias
655 7 $a Electronic books. $2 lcsh $3 542853
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690 $a 0216
690 $a 0364
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773 0 $t Dissertations Abstracts International $g 84-08B.
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