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Modeling Collective Motion of Comple...

Arizona State University.

Modeling Collective Motion of Complex Systems using Agent-based Models and Macroscopic Models.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Modeling Collective Motion of Complex Systems using Agent-based Models and Macroscopic Models.
Author:	Jamous, Sara Sami.
Published:	Ann Arbor : ProQuest Dissertations & Theses, 2019
Description:	106 p.
Notes:	Source: Dissertations Abstracts International, Volume: 81-03, Section: B.
Notes:	Advisor: Motsch, Sebastien.
Contained By:	Dissertations Abstracts International81-03B.
Subject:	Applied mathematics.
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=22588140
ISBN:	9781085691734

Modeling Collective Motion of Complex Systems using Agent-based Models and Macroscopic Models.
Jamous, Sara Sami.

Modeling Collective Motion of Complex Systems using Agent-based Models and Macroscopic Models. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 106 p.

Source: Dissertations Abstracts International, Volume: 81-03, Section: B.

Thesis (Ph.D.)--Arizona State University, 2019.

This item must not be sold to any third party vendors.

The main objective of mathematical modeling is to connect mathematics with other scientific fields. Developing predictable models help to understand the behavior of biological systems. By testing models, one can relate mathematics and real-world experiments. To validate predictions numerically, one has to compare them with experimental data sets. Mathematical modeling can be split into two groups: microscopic and macroscopic models. Microscopic models described the motion of so-called agents (e.g. cells, ants) that interact with their surrounding neighbors. The interactions among these agents form at a large scale some special structures such as flocking and swarming. One of the key questions is to relate the particular interactions among agents with the overall emerging structures. Macroscopic models are precisely designed to describe the evolution of such large structures. They are usually given as partial differential equations describing the time evolution of a density distribution (instead of tracking each individual agent). For instance, reaction-diffusion equations are used to model glioma cells and are being used to predict tumor growth. This dissertation aims at developing such a framework to better understand the complex behavior of foraging ants and glioma cells.

ISBN: 9781085691734Subjects--Topical Terms:

377601
Applied mathematics.

Modeling Collective Motion of Complex Systems using Agent-based Models and Macroscopic Models.
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502 $a Thesis (Ph.D.)--Arizona State University, 2019.
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520 $a The main objective of mathematical modeling is to connect mathematics with other scientific fields. Developing predictable models help to understand the behavior of biological systems. By testing models, one can relate mathematics and real-world experiments. To validate predictions numerically, one has to compare them with experimental data sets. Mathematical modeling can be split into two groups: microscopic and macroscopic models. Microscopic models described the motion of so-called agents (e.g. cells, ants) that interact with their surrounding neighbors. The interactions among these agents form at a large scale some special structures such as flocking and swarming. One of the key questions is to relate the particular interactions among agents with the overall emerging structures. Macroscopic models are precisely designed to describe the evolution of such large structures. They are usually given as partial differential equations describing the time evolution of a density distribution (instead of tracking each individual agent). For instance, reaction-diffusion equations are used to model glioma cells and are being used to predict tumor growth. This dissertation aims at developing such a framework to better understand the complex behavior of foraging ants and glioma cells.
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