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


Abstract 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 a... (more)
Created Date 2019
Contributor Jamous, Sara Sami (Author) / Motsch, Sebastien (Advisor) / Armbruster, Dieter (Committee member) / Camacho, Erika (Committee member) / Moustaoui, Mohamed (Committee member) / Platte, Rodrigo (Committee member) / Arizona State University (Publisher)
Subject Applied mathematics / Agent Based Models / Collective motion / Data-model comparison / Glioma cells / Macroscopic Models / Modeling
Type Doctoral Dissertation
Extent 105 pages
Language English
Copyright
Note Doctoral Dissertation Applied Mathematics 2019
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS


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Description Dissertation/Thesis