This course offers an in-depth exploration of modeling methods and simulation tools for a diverse array of natural phenomena. Through practical assignments, students will gain hands-on experience in developing short programs to solve simple problems. The curriculum covers topics such as dynamical systems, cellular automata, fluid flow modeling, and discrete events simulation.
The course provides a basic guideline towards different methodologies that can be applied to solve a wide spectrum of problems, empowering learners to select the most suitable approach for their specific needs. With a focus on practical application, participants will not only gain proficiency in programming with Python 3 but also learn about high-performance computing for modeling and simulation.
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This course is structured into eight comprehensive modules covering topics such as dynamical systems, cellular automata, fluid flow modeling, and discrete events simulation. Participants will gain practical experience in programming with Python 3 and high-performance computing for modeling and simulation.
This module provides an introduction to modeling methods and simulation tools, covering general concepts, modeling space and time, bio-medical modeling, and Monte Carlo methods. Participants will gain insight into various methodologies applicable to solving a wide range of problems.
Participants will delve into high-performance computing for modeling and simulation, learning about code optimization, parallelism, and Python 3. The module also includes practical projects such as Piles and Class:Integration, enabling hands-on application of programming concepts.
This module focuses on dynamical systems and numerical integration, exploring topics such as the random walk, population growth, balance equations, and numerical integration of differential equations. Participants will gain practical experience in solving problems related to dynamical systems.
Participants will gain an understanding of cellular automata, including its definition, historical background, and application in modeling traffic and complex systems. The module also includes a project on the Parity Rule, allowing learners to apply their knowledge in a practical setting.
This module provides an overview of computational fluid dynamics and delves into lattice Boltzmann modeling of fluid flow. Participants will gain practical experience in simulating flow around obstacles and working with collision invariants.
Participants will explore the dynamics of particles and point-like objects, including Newton's laws of motion, time-integration of equations, and the n-body problem. The module also includes a project on building a Barnes-Hut Galaxy Simulator, applying the learned concepts in a practical context.
This module introduces participants to discrete events simulation, covering topics such as traffic intersection and volcano ballistics. Practical projects, including modeling traffic lights, enable learners to apply their knowledge to real-world scenarios.
Participants will explore the motivation behind agent-based models, learning about agents, multi-agent systems, and their implementation. Projects on Ants Corpse clustering and Bacteria chemotaxy provide practical application of the concepts learned in the module.
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