東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Utilizing Data-Driven Modeling to Ch...

Su, Wei-Hung,

FindBook

Google Book

Amazon

博客來

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.
Contained By:	Dissertations Abstracts International85-11B.
標題:	Mathematics. -
電子資源:	https://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:

515831
Mathematics.
Subjects--Index Terms:

Data-driven modeling

Utilizing Data-Driven Modeling to Characterize Biological Systems /
LDR:03434nmm a22004693i 4500 001 2428911
005 20250420214349.5
006 m o d
007 cr|nu||||||||
008 261011s2023 miu||||||m |||||||eng d
020 $a 9798382604213
035 $a (MiAaPQD)AAI31340961
035 $a (MiAaPQD)OhioLINKosu1681941476945444
035 $a AAI31340961
040 $a MiAaPQD $b eng $c MiAaPQD $e rda
100 1 $a Su, Wei-Hung, $e author. $3 3816435
245 1 0 $a Utilizing Data-Driven Modeling to Characterize Biological Systems / $c Wei-Hung Su.
264 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2023
300 $a 1 electronic resource (91 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 85-11, Section: B.
500 $a Advisors: Xiu, Dongbin Committee members: Best, Janet; Xing, Yulong.
502 $b Ph.D. $c The Ohio State University $d 2023.
520 $a 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.
546 $a English
590 $a School code: 0168
650 4 $a Mathematics. $3 515831
650 4 $a Applied mathematics. $3 2122814
650 4 $a Computer science. $3 523869
650 4 $a Computational physics. $3 3343998
650 4 $a Bioinformatics. $3 553671
653 $a Data-driven modeling
653 $a Biological systems
653 $a Dynamical systems
653 $a Deep neural networks
653 $a Flocking models
690 $a 0405
690 $a 0984
690 $a 0216
690 $a 0364
690 $a 0715
710 2 $a The Ohio State University. $b Mathematics. $e degree granting institution. $3 3816436
720 1 $a Xiu, Dongbin $e degree supervisor.
773 0 $t Dissertations Abstracts International $g 85-11B.
790 $a 0168
791 $a Ph.D.
792 $a 2023
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31340961