東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Chaos, Observability and Symplectic ...

Rey, Daniel.

Linked to FindBook

Google Book

Amazon

博客來

Chaos, Observability and Symplectic Structure in Optimal Estimation.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Chaos, Observability and Symplectic Structure in Optimal Estimation./
Author:	Rey, Daniel.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2017,
Description:	185 p.
Notes:	Source: Dissertation Abstracts International, Volume: 79-01(E), Section: B.
Contained By:	Dissertation Abstracts International79-01B(E).
Subject:	Biophysics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10281245
ISBN:	9780355313659

Chaos, Observability and Symplectic Structure in Optimal Estimation.
Rey, Daniel.

Chaos, Observability and Symplectic Structure in Optimal Estimation. - Ann Arbor : ProQuest Dissertations & Theses, 2017 - 185 p.

Source: Dissertation Abstracts International, Volume: 79-01(E), Section: B.

Thesis (Ph.D.)--University of California, San Diego, 2017.

Observation, estimation and prediction are universal challenges that become especially difficult when the system under consideration is dynamical and chaotic. Chaos injects dynamical noise into the estimation process that must be suppressed to satisfy the necessary conditions for success: namely, synchronization of the estimate and the observed data. The ability to control the growth of errors is constrained by the spatiotemporal resolution of the observations, and often exhibits critical thresholds below which the probability of success becomes effectively zero. This thesis examines the connections between these limits and basic issues of complexity, conditioning, and instability in the observation and forecast models. The results suggest several new ideas to improve the collaborative design of combined observation, analysis, and forecast systems. Among these, the most notable is perhaps the fundamental role that symplectic structure plays in the remarkable observational efficiency of Kalman-based estimation methods.

ISBN: 9780355313659Subjects--Topical Terms:

518360
Biophysics.

Chaos, Observability and Symplectic Structure in Optimal Estimation.
LDR:02007nmm a2200313 4500 001 2155429
005 20180426091046.5
008 190424s2017 ||||||||||||||||| ||eng d
020 $a 9780355313659
035 $a (MiAaPQ)AAI10281245
035 $a (MiAaPQ)ucsd:16475
035 $a AAI10281245
040 $a MiAaPQ $c MiAaPQ
100 1 $a Rey, Daniel. $3 3343165
245 1 0 $a Chaos, Observability and Symplectic Structure in Optimal Estimation.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2017
300 $a 185 p.
500 $a Source: Dissertation Abstracts International, Volume: 79-01(E), Section: B.
500 $a Adviser: Henry DI Abarbanel.
502 $a Thesis (Ph.D.)--University of California, San Diego, 2017.
520 $a Observation, estimation and prediction are universal challenges that become especially difficult when the system under consideration is dynamical and chaotic. Chaos injects dynamical noise into the estimation process that must be suppressed to satisfy the necessary conditions for success: namely, synchronization of the estimate and the observed data. The ability to control the growth of errors is constrained by the spatiotemporal resolution of the observations, and often exhibits critical thresholds below which the probability of success becomes effectively zero. This thesis examines the connections between these limits and basic issues of complexity, conditioning, and instability in the observation and forecast models. The results suggest several new ideas to improve the collaborative design of combined observation, analysis, and forecast systems. Among these, the most notable is perhaps the fundamental role that symplectic structure plays in the remarkable observational efficiency of Kalman-based estimation methods.
590 $a School code: 0033.
650 4 $a Biophysics. $3 518360
650 4 $a Computer science. $3 523869
650 4 $a Applied mathematics. $3 2122814
690 $a 0786
690 $a 0984
690 $a 0364
710 2 $a University of California, San Diego. $b Physics. $3 1035951
773 0 $t Dissertation Abstracts International $g 79-01B(E).
790 $a 0033
791 $a Ph.D.
792 $a 2017
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10281245