Collective Dynamics of Complex Systems
SSIE-523-20
Binghamton University
- Semester
- Spring 2025
- Instructor
- Hiroki Sayama
- Start Date
- 01-21-2025
- Total Credits
- 3
- Call to Register
- 607-777-6088
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You will learn the same curriculum as our on-campus students
Introduces students to the study of collective dynamics demonstrated by various natural, social and artificial complex systems, i.e., systems made of a massive amount of lower-level components interacting with each other in a nonlinear way. Discusses several computational modeling frameworks, including agent-based models (particle models, ecological and evolutionary models, game-theoretic models), complex network models (small-world and scale-free networks, dynamical networks, adaptive networks), and spatial models (cellular automata, partial differential equations). Also discusses mathematical concepts and tools to analyze and understand their behavior, e.g., mean-field approximation, linear stability analysis, scaling, renormalization, bifurcation, chaos, pattern formation, and phase transition. Python will be used as a primary computer programming language for modeling and simulation. Prior computer programming experience is helpful, but not strictly required.
Prerequisites:
Graduate standing and basic knowledge of calculus, linear algebra and probability theory, or consent of instructor
Course Area: Engineering
Level: Graduate
Dates: 01-21-2025 - 05-07-2025
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- Instructor: Hiroki Sayama
- Local Course ID: 25SP_BIN_SSIE523_20
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