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

A hybrid approach of physical laws a...

Hofleitner, Aude.

Linked to FindBook

Google Book

Amazon

博客來

A hybrid approach of physical laws and data-driven modeling for estimation: the example of queuing networks.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	A hybrid approach of physical laws and data-driven modeling for estimation: the example of queuing networks./
Author:	Hofleitner, Aude.
Description:	211 p.
Notes:	Source: Dissertation Abstracts International, Volume: 75-01(E), Section: B.
Contained By:	Dissertation Abstracts International75-01B(E).
Subject:	Computer Science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3593849
ISBN:	9781303373169

A hybrid approach of physical laws and data-driven modeling for estimation: the example of queuing networks.
Hofleitner, Aude.

A hybrid approach of physical laws and data-driven modeling for estimation: the example of queuing networks. - 211 p.

Source: Dissertation Abstracts International, Volume: 75-01(E), Section: B.

Thesis (Ph.D.)--University of California, Berkeley, 2013.

Mathematical models are a mathematical abstraction of the physical reality which is of great importance to understand the behavior of a system, make estimations and predictions and so on. They range from models based on physical laws to models learned empirically, as measurements are collected, and referred to as data-driven models. A model is based on a series of choices which influence its complexity and realism. These choices represent trade-offs between different competing objectives including interpretability, scalability, accuracy, adequation to the available data, robustness or computational complexity. The thesis investigates the advantages and disadvantages of models based on physical laws versus data-driven models through the example of signalized queuing networks such as urban transportation networks.

ISBN: 9781303373169Subjects--Topical Terms:

626642
Computer Science.

A hybrid approach of physical laws and data-driven modeling for estimation: the example of queuing networks.
LDR:03120nam a2200289 4500 001 1960585
005 20140623111237.5
008 150210s2013 ||||||||||||||||| ||eng d
020 $a 9781303373169
035 $a (MiAaPQ)AAI3593849
035 $a AAI3593849
040 $a MiAaPQ $c MiAaPQ
100 1 $a Hofleitner, Aude. $3 2096273
245 1 2 $a A hybrid approach of physical laws and data-driven modeling for estimation: the example of queuing networks.
300 $a 211 p.
500 $a Source: Dissertation Abstracts International, Volume: 75-01(E), Section: B.
500 $a Adviser: Alexandre M. Bayen.
502 $a Thesis (Ph.D.)--University of California, Berkeley, 2013.
520 $a Mathematical models are a mathematical abstraction of the physical reality which is of great importance to understand the behavior of a system, make estimations and predictions and so on. They range from models based on physical laws to models learned empirically, as measurements are collected, and referred to as data-driven models. A model is based on a series of choices which influence its complexity and realism. These choices represent trade-offs between different competing objectives including interpretability, scalability, accuracy, adequation to the available data, robustness or computational complexity. The thesis investigates the advantages and disadvantages of models based on physical laws versus data-driven models through the example of signalized queuing networks such as urban transportation networks.
520 $a The dynamics of conservation flow networks are accurately represented by a first order partial differential equation. Using Hamilton-Jacobi theory, the thesis underlines the importance to leverage physical laws to reconstruct missing information (e.g. signal or bottleneck characteristics) and estimate the state of the network at any time and location. Noise and uncertainty in the measurements can be integrated in the model. When measurements are sparse, the state of the network cannot be estimated at every time and location on the network. Instead, the thesis shows how to leverage other characteristics, such as periodicity. From deterministic dynamics, the thesis derives the probability distribution functions of physical entities (e.g. waiting time, density) by marginalizing the periodic variable. Using a Dynamic Bayesian Network formulation and exploiting the convexity structure of the system, the thesis shows how this modeling leads to realistic estimations and predictions, even when little measurements are available. Finally, the thesis investigates how sparse modeling and dimensionality reduction can provide insights on the large scale behavior of the network. Large scale dynamics and patterns are hard to model accurately based on physical laws. They can be discovered through data mining algorithms and integrated into physical models.
590 $a School code: 0028.
650 4 $a Computer Science. $3 626642
650 4 $a Applied Mathematics. $3 1669109
690 $a 0984
690 $a 0364
710 2 $a University of California, Berkeley. $b Electrical Engineering and Computer Sciences. $3 2096274
773 0 $t Dissertation Abstracts International $g 75-01B(E).
790 $a 0028
791 $a Ph.D.
792 $a 2013
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3593849