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Nonlinear Dynamics and Optimal Learning Towards Quality and Reliability Assurance for Complex Dynamical Systems.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Nonlinear Dynamics and Optimal Learning Towards Quality and Reliability Assurance for Complex Dynamical Systems./
Author:	Che, Yiming.
Description:	1 online resource (128 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 84-12, Section: B.
Contained By:	Dissertations Abstracts International84-12B.
Subject:	Systems science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30426542click for full text (PQDT)
ISBN:	9798379731151

Nonlinear Dynamics and Optimal Learning Towards Quality and Reliability Assurance for Complex Dynamical Systems.
Che, Yiming.

Nonlinear Dynamics and Optimal Learning Towards Quality and Reliability Assurance for Complex Dynamical Systems. - 1 online resource (128 pages)

Source: Dissertations Abstracts International, Volume: 84-12, Section: B.

Thesis (Ph.D.)--State University of New York at Binghamton, 2023.

Includes bibliographical references

We address the issue of efficiency in quality and reliability assurance for complex dynamical systems using active learning. The problem can be regarded as contour, also called iso-surface, estimation. In some cases, obtaining the true iso-surface according to the traditional one-shot design is inefficient because computer simulations can be cumbersome. Hence, we propose the method that combines a cheap surrogate model (low-fidelity model) and high-fidelity computer simulations to efficiently approximate the contour of interest. The paradigm is called active learning. The core idea is that not all the data result in the significant surrogate model update and we design the strategies to find "useful" data to speed up the convergence of surrogate models with the least number of training data. Consequently, the number of expensive high-fidelity computer simulations are significantly reduced.

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

Mode of access: World Wide Web

ISBN: 9798379731151Subjects--Topical Terms:

3168411
Systems science.
Subjects--Index Terms:

Reliability assuranceIndex Terms--Genre/Form:

542853
Electronic books.

Nonlinear Dynamics and Optimal Learning Towards Quality and Reliability Assurance for Complex Dynamical Systems.
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245 1 0 $a Nonlinear Dynamics and Optimal Learning Towards Quality and Reliability Assurance for Complex Dynamical Systems.
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300 $a 1 online resource (128 pages)
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500 $a Source: Dissertations Abstracts International, Volume: 84-12, Section: B.
500 $a Advisor: Cheng, Changqing.
502 $a Thesis (Ph.D.)--State University of New York at Binghamton, 2023.
504 $a Includes bibliographical references
520 $a We address the issue of efficiency in quality and reliability assurance for complex dynamical systems using active learning. The problem can be regarded as contour, also called iso-surface, estimation. In some cases, obtaining the true iso-surface according to the traditional one-shot design is inefficient because computer simulations can be cumbersome. Hence, we propose the method that combines a cheap surrogate model (low-fidelity model) and high-fidelity computer simulations to efficiently approximate the contour of interest. The paradigm is called active learning. The core idea is that not all the data result in the significant surrogate model update and we design the strategies to find "useful" data to speed up the convergence of surrogate models with the least number of training data. Consequently, the number of expensive high-fidelity computer simulations are significantly reduced.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2023
538 $a Mode of access: World Wide Web
650 4 $a Systems science. $3 3168411
650 4 $a Industrial engineering. $3 526216
650 4 $a Mathematics. $3 515831
653 $a Reliability assurance
653 $a Dynamical systems
653 $a Nonlinear dynamics
653 $a Optimal learning
655 7 $a Electronic books. $2 lcsh $3 542853
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690 $a 0546
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710 2 $a ProQuest Information and Learning Co. $3 783688
710 2 $a State University of New York at Binghamton. $b Systems Science Industrial Engineering. $3 2104041
773 0 $t Dissertations Abstracts International $g 84-12B.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30426542 $z click for full text (PQDT)