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Nonlinear Inference in Partially Obs...

Rozdeba, Paul J.

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

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Nonlinear Inference in Partially Observed Physical Systems and Deep Neural Networks./
作者:	Rozdeba, Paul J.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
面頁冊數:	168 p.
附註:	Source: Dissertation Abstracts International, Volume: 79-07(E), Section: B.
Contained By:	Dissertation Abstracts International79-07B(E).
標題:	Computational physics. -
電子資源:	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.
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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.
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