Modelling And Simulation Cookbook

Modelling and simulation is a key interdisciplinary field that enables causal system knowledge and data to be combined and used for decision-support in various applications ranging from engineering to social sciences. The Cookbook of Modelling and Simulation offers an interactive introduction to the key concepts of this discipline and explains them using targeted case-studies. The cookbook is designed for modelling and simulation education and is aimed at those who wish to develop or extend their knowledge and skills in this field. However, thanks to the wide range of case-studies covered, the scope of the Cookbook extends far beyond this. The corresponding implementations are ready-to-use and can potentially serve as blueprints even for experienced modellers.

Motivation¶

The Modelling and Simulation Cookbook has been developed primarily to support the teaching of the subject at TU Wien and is intended, in particular, to serve as an interactive lecture notes for the two courses Modelling and Simulation and Advanced Modelling and Simulation.

In addition, the Cookbook aims to present selected content from the successful blended-learning MATLAB server MMT Winkler et al. (2011) and make it available to a wider audience in the form of pedagogically adapted Jupyter notebooks. In this respect, the cookbook also serves as an online laboratory where readers can experiment with simulation models that have already been implemented and validated, without having to implement them themselves.

Authors¶

The Modelling and Simulation Cookbook was developed by Martin Bicher martin.bicher@tuwien.ac.at supported by Hanno Bösch and Aida Daniela Beccera Lopez.

Structure¶

The Cookbook’s notebooks are organised into broader chapters that follow a didactic red-thread. These are

• Introduction to Modelling and Simulation, giving an introduction into base terminlogy,

• Modelling Methods, presenting the most importand and common strategies for developing causal models,

• Simulation Methods, showing important numerical and computer methods for running simulations,

• Parametrisation, describing methods for finding reasonable parameter values, and

• Analysis, Verification and Validation, describing methods for model analysis, model verification and validation.

The included notebooks have clearly defined teaching and learning objective and most of them also contain embedded case studies designed to aid direct understanding of the learning content. Pure case-study notebooks (i.e. online experiments with validated models) however are not visible in the static version of the Cookbook. They can be accessed via Binderhub or after locally cloning the Cookbook repository (see Running the Notebooks).

Acknowledments¶

The Cookbook is partially inspired by existing historic and mostly unpublished lecture material at TU Wien. In particular the author gratefully acknowledge Inge Troch, Felix Breitenecker, Andreas Körner and Niki Popper for sharing their material for consultation. Full respect is given to their intellectual property rights and scholarly contributions. We want to state that this Cookbook was independently developed and does not reproduce any unpublished material.

Running the Notebooks¶

Running on TU Cookbooks Binder¶

Go to TU Cookbooks Binder: https://binder.cookbooks.datalab.tuwien.ac.at/

Running on Your Own Machine¶

If you are interested in running this material locally on your computer, you will need to follow this workflow:

1. Clone the https://gitlab.tuwien.ac.at/cookbooks/public/modelling_and_simulation_cookbook repository:

    git clone https://gitlab.tuwien.ac.at/cookbooks/modelling_and_simulation_cookbook

2. Move into the modelling_and_simulation_cookbook directory

   cd modelling_and_simulation_cookbook

3. Create a virtual environment with all the required libraries and dependencies For that a couple of Options are available:

   • uv

   • conda

   • Docker

uv¶

Create and activate your virtual environment

uv sync --all-extras

Start up Jupyterlab Server in the notebooks directory

uv run jupyter lab notebooks/

Docker¶

When available simply run docker compose to start a Jupyter Lab instance.

1. Start Jupyter Lab from a Docker container:

   docker compose up

2. Copy Jupyter Lab URL to web browser:

Stop and remove the container

1. Stop Docker container with CTRL + C

2. Remove container:

   docker compose down