Online or onsite, instructor-led live Agent Based Modeling training courses demonstrate through interactive hands-on practice the fundamentals of Agent Based Modeling.
Agent Based Modeling training is available as "online live training" or "onsite live training". Online live training (aka "remote live training") is carried out by way of an interactive, remote desktop. Onsite live Agent Based Modeling training can be carried out locally on customer premises in the US or in NobleProg corporate training centers in the US.
NobleProg -- Your Local Training Provider
The trainer well prepared the course material beforehand and the session was very flexible and arranged to meet the trainee's interests. The management staffs were also around during the course to help us. The project was well managed in a friendly atmosphere throughout.
Course: Repast - Agent Based Modeling and Simulation (ABMS)
Agent Based Modeling Course Outlines
- Learn and understand the fundamental concepts of Digital Twin.
- Understand the Digital Twin types and life cycle.
- Use Digital Twin to effectively manage research data and design models.
- Install and configure the development environment needed to start modeling in Python.
- Quickly create an agent-based model using Mesa's built-in core components.
- Expand the complexity of the model.
- Visualize agent activity in real-time inside a browser.
- Analyze the results of the model interactively using Python data analysis tools.
- Integrate the model with other Python systems such as machine learning applications.
- Introduce participants to principles and concepts of agent-based modeling and simulation.
- Develop participants’ capacity to read and understand agent modeling programming code.
- Equip participants with knowledge, so they understand the importance of accurate and precise modeling.
- Increase the participants’ understanding of systems of their own using the Agent-based simulation.
- Develop a better capacity to use and code agent-based systems using repast, reLogo.
- Install NetLogo.
- Develop an agent-based model.
- Visualize agent activity.
- Analyze the results of the model.
- Test and document the agent-based simulation model.