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
Testimonials
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.
Kikuko Shoyama
Course: Repast - Agent Based Modeling and Simulation (ABMS)
Agent Based Modeling Course Outlines
Course Name
Duration
Overview
Course Name
Duration
Overview
14 hours
This instructor-led, live training in the US (online or onsite) is aimed at anyone who wishes to learn how to use Digital Twin in monitoring and analyzing products and processes before building and deploying the actual product.
By the end of this training, participants will be able to:
- 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.
14 hours
This instructor-led, live training in the US (online or onsite) is aimed at investigators who wish to use Mesa to create Agent Based Models in a Python environment.
By the end of this training, participants will be able to:
- 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.
28 hours
The Repast Suite is a family of advanced, free, and open source agent-based modeling and simulation libraries that have collectively been under continuous development for over 10 years. This four day course in the US (online or onsite) explains in detail how to create simulation for agent-based systems using the Repast environment.
Course Objectives
- 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.
21 hours
This instructor-led, live training in the US (online or onsite) is aimed at investigators who wish to use NetLogo to create Agent Based Models (ABM).
By the end of this training, participants will be able to:
- Install NetLogo.
- Develop an agent-based model.
- Visualize agent activity.
- Analyze the results of the model.
- Test and document the agent-based simulation model.
Last Updated:
Upcoming Agent Based Modeling Courses
Agent Based Modeling (ABM) with NetLogo
04/05/2023 - 09:30
CT, Hartford - Downtown
21 hours
Fundamentals of Digital Twin
04/19/2023 - 09:30
CT, Hartford - Downtown
14 hours
Agent Based Modeling (ABM) with NetLogo
05/03/2023 - 09:30
CT, Hartford - Downtown
21 hours
Agent Based Modeling (ABM) with NetLogo
05/17/2023 - 09:30
CT, Hartford - Downtown
21 hours
Agent Based Modeling (ABM) with Mesa and Python
05/31/2023 - 09:30
CT, Hartford - Downtown
14 hours
Agent Based Modeling (ABM) with NetLogo
06/14/2023 - 09:30
CT, Hartford - Downtown
21 hours
Agent Based Modeling (ABM) with Mesa and Python
06/28/2023 - 09:30
CT, Hartford - Downtown
14 hours
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