Graduation Semester and Year
Fall 2024
Language
English
Document Type
Thesis
Degree Name
Master of Science in Computer Science
Department
Computer Science and Engineering
First Advisor
Dr. Nicholas R. Gans
Second Advisor
Dr. Diego Patiño
Third Advisor
Dr. William Beksi
Abstract
Our work investigates the use of Graph Neural Networks (GNNs) for modeling complex dynamic systems. Understanding dynamic systems is essential for comprehending how physical and social interactions in real-world systems evolve over time and how they can be influenced or controlled. Conventional techniques often use differential equations and numerical methods, which are computationally expensive and demand detailed knowledge of the system’s physical behavior. GNNs provide an alternative by adopting a data-driven approach that learns directly from data, making them especially valuable for systems without closed-form solutions. This work aims to harness GNNs to predict and simulate dynamic behaviors in graph-based systems, including groups of people, animals, human-robot interactions, and vehicle traffic. We have enhanced an existing mass-spring simulation framework by incorporating more sophisticated dynamics, including varied node and edge characteristics like mass, spring constants, damping coefficients, and resting lengths, to more accurately model complex physical systems. To better isolate strengths, limitations and areas for improvement, we gradually increase the complexity of the system, introducing changes one at a time. We will present key results and observations from these enhanced simulations, underscoring the effectiveness of GNNs in capturing and simulating dynamic behaviors in complex systems.
Keywords
Graph neural networks, Dynamic systems, Modeling physical systems, Graph interaction prediction, Trajectory prediction, Mass spring damper system, modeling Complex systems, Variational autoencoder
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Udupa, Shruti, "MODELING COMPLEX DYNAMIC SYSTEMS USING GRAPH NEURAL NETWORKS" (2024). Computer Science and Engineering Theses. 524.
https://mavmatrix.uta.edu/cse_theses/524