Agent Based Modeling Training in San Antonio

This instructor-led, live training in San Antonio (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.

This instructor-led, live training in San Antonio (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.

This instructor-led, live training in San Antonio (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.

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 San Antonio (online or onsite) explains in detail how to create simulation for agent-based systems using the Repast environment.

Course Objectives

1. Introduce participants to principles and concepts of agent-based modeling and simulation.
2. Develop participants’ capacity to read and understand agent modeling programming code.
3. Equip participants with knowledge, so they understand the importance of accurate and precise modeling.
4. Increase the participants’ understanding of systems of their own using the Agent-based simulation.
5. Develop a better capacity to use and code agent-based systems using repast, reLogo.