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Utilizing Data-Driven Modeling to Characterize Biological Systems /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Utilizing Data-Driven Modeling to Characterize Biological Systems /Wei-Hung Su.
作者:	Su, Wei-Hung,
面頁冊數:	1 electronic resource (91 pages)
附註:	Source: Dissertations Abstracts International, Volume: 85-11, Section: B.
附註:	Advisors: Xiu, Dongbin Committee members: Best, Janet; Xing, Yulong.
Contained By:	Dissertations Abstracts International85-11B.
標題:	Mathematics.
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31340961
ISBN:	9798382604213

Utilizing Data-Driven Modeling to Characterize Biological Systems /
Su, Wei-Hung,

Utilizing Data-Driven Modeling to Characterize Biological Systems /Wei-Hung Su. - 1 electronic resource (91 pages)

Source: Dissertations Abstracts International, Volume: 85-11, Section: B.

This dissertation presents three novel methodologies for Data-Driven modeling to biological systems with complex dynamical behavior. First of all, a numerical procedure based on deep learning methods, particularly residual, and flow-map learning tools for dynamical systems is presented to accurately construct numerical dynamic system from measurement data and incorporate unknown parameters. The trained deep neural network model can be used as a predictive tool to produce system predictions of different settings and conduct detailed analyses of the underlying process. In this section, three different biological models are utilized to display the performance of the proposed method. Secondly, a deep neural network structure with memory terms and recurrent structures is proposed for modeling biological systems with partially observed data containing hidden parameters. Through several representative biological problems, the methodology is shown to capture qualitative dynamical behavior changes in the system, even when the parameters controlling such changes are completely unknown. The learned deep neural network model effectively creates a "closed" model involving only the observables when such a closed-form model does not exist mathematically. Last but not least, the focus shifts towards particle movement modeling, and the proposed coarse density methodology is used to identify the governing equation of the density dynamic of particle systems. By utilizing the recurrent DNN with memory terms, the methodology is shown to be effective in recovering the underlying governing equations in the second part of the thesis. The flocking and traffic models are used to showcase the methodology and demonstrate how the learned deep neural network can represent the density dynamic of the original model. Furthermore, the relation between the Lagrange and Euler framework is also discussed using the traffic model.

English

ISBN: 9798382604213Subjects--Topical Terms:

184409
Mathematics.
Subjects--Index Terms:

Data-driven modeling

Utilizing Data-Driven Modeling to Characterize Biological Systems /
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