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

Nonlinear Inference in Partially Obs...

Rozdeba, Paul J.

Linked to FindBook

Google Book

Amazon

博客來

Nonlinear Inference in Partially Observed Physical Systems and Deep Neural Networks.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Nonlinear Inference in Partially Observed Physical Systems and Deep Neural Networks./
Author:	Rozdeba, Paul J.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
Description:	168 p.
Notes:	Source: Dissertation Abstracts International, Volume: 79-07(E), Section: B.
Contained By:	Dissertation Abstracts International79-07B(E).
Subject:	Computational physics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10743524
ISBN:	9780355593815

Nonlinear Inference in Partially Observed Physical Systems and Deep Neural Networks.
Rozdeba, Paul J.

Nonlinear Inference in Partially Observed Physical Systems and Deep Neural Networks. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 168 p.

Source: Dissertation Abstracts International, Volume: 79-07(E), Section: B.

Thesis (Ph.D.)--University of California, San Diego, 2018.

The problem of model state and parameter estimation is a significant challenge in nonlinear systems. Due to practical considerations of experimental design, it is often the case that physical systems are partially observed, meaning that data is only available for a subset of the degrees of freedom required to fully model the observed system's behaviors and, ultimately, predict future observations. Estimation in this context is highly complicated by the presence of chaos, stochasticity, and measurement noise in dynamical systems.

ISBN: 9780355593815Subjects--Topical Terms:

3343998
Computational physics.

Nonlinear Inference in Partially Observed Physical Systems and Deep Neural Networks.
LDR:02752nmm a2200349 4500 001 2166175
005 20181203094032.5
008 190424s2018 ||||||||||||||||| ||eng d
020 $a 9780355593815
035 $a (MiAaPQ)AAI10743524
035 $a (MiAaPQ)ucsd:17143
035 $a AAI10743524
040 $a MiAaPQ $c MiAaPQ
100 1 $a Rozdeba, Paul J. $3 3354279
245 1 0 $a Nonlinear Inference in Partially Observed Physical Systems and Deep Neural Networks.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2018
300 $a 168 p.
500 $a Source: Dissertation Abstracts International, Volume: 79-07(E), Section: B.
500 $a Adviser: Henry D. I. Abarbanel.
502 $a Thesis (Ph.D.)--University of California, San Diego, 2018.
520 $a The problem of model state and parameter estimation is a significant challenge in nonlinear systems. Due to practical considerations of experimental design, it is often the case that physical systems are partially observed, meaning that data is only available for a subset of the degrees of freedom required to fully model the observed system's behaviors and, ultimately, predict future observations. Estimation in this context is highly complicated by the presence of chaos, stochasticity, and measurement noise in dynamical systems.
520 $a One of the aims of this dissertation is to simultaneously analyze state and parameter estimation in as a regularized inverse problem, where the introduction of a model makes it possible to reverse the forward problem of partial, noisy observation; and as a statistical inference problem using data assimilation to transfer information from measurements to the model states and parameters. Ultimately these two formulations achieve the same goal. Similar aspects that appear in both are highlighted as a means for better understanding the structure of the nonlinear inference problem.
520 $a An alternative approach to data assimilation that uses model reduction is then examined as a way to eliminate unresolved nonlinear gating variables from neuron models. In this formulation, only measured variables enter into the model, and the resulting errors are themselves modeled by nonlinear stochastic processes with memory.
520 $a Finally, variational annealing, a data assimilation method previously applied to dynamical systems, is introduced as a potentially useful tool for understanding deep neural network training in machine learning by exploiting similarities between the two problems.
590 $a School code: 0033.
650 4 $a Computational physics. $3 3343998
650 4 $a Theoretical physics. $3 2144760
650 4 $a Computer science. $3 523869
690 $a 0216
690 $a 0753
690 $a 0984
710 2 $a University of California, San Diego. $b Physics. $3 1035951
773 0 $t Dissertation Abstracts International $g 79-07B(E).
790 $a 0033
791 $a Ph.D.
792 $a 2018
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10743524