MATHMOD & ASIM Tutorials

In the afternoon of July 26, 2022, a number of tutorials will be jointly organized by MATHMOD 2022 and ASIM 2022. The tutorials are assigned to one of two time slots (B1 13:30-15:30 and B2 16:00-18:00).

  • Teaching with MATLAB (B1)
  • MATLAB Biomedical Signals – AI Hands-On Workshop (B2)
  • MATLAB DEVS – Discrete Event Simulation (B1)
  • Modelica Thermofluid Stream Library (B1)
  • Modelica System Dynamics Library (B2)
  • Agent-based Modelling for Health Science (B1)
  • Reinforcement Learning with Applications (B2)

All tutorials are open to MATHMOD 2022 participants, ASIM 2022 participants, and TU Wien members. However, a separate registration (independent from conference registration) is necessary.

Tutorials will be (partly) recorded in order to allow participation at parallel tutorials. Recorded tutorials and tutorial handouts are available to registered participants also after the tutorial for at least a month.

Tutorial details

Teaching with MATLAB

Create engaging, scalable instructions with MathWorks online learning tools. 
Highlights include Creating Interactive Scripts and App, Moving to the Cloud, Sharing Content with course collaborators and students, Helping Students learn MATLAB, create and automatically grade MATLAB coding assignments with MATLAB Grader – incl. an applied example from TU Wien.

Kathi Kugler, The MathWorks; Clara Horvath, Iris Feldhammer, Andreas Körner, TU Wien 

MATLAB Biomedical Signals

Artificial Intelligence’s (AI) primary aim in a health-related environment is to provide clinical decision and diagnostic support by analyzing relationships between treatment options and patient outcomes. AI has also been developed for patient monitoring and care, drug development and disease prevention.  Medical device manufacturers are using these technologies to innovate their products to better assist health care providers and improve patient care.
In this workshop, you will learn how to develop AI applications using MATLAB on the vast data generated during the delivery of health care every day. You will find out about tools and fundamental approaches for developing advanced predictive models on biomedical signals. We will cover the entire AI pipeline from all the way from signal exploration to deployment using both machine learning and deep learning approaches. In this hands-on workshop, you will write code and use MATLAB Online to: 1. Annotate time series biomedical signals automatically, 2.Train AI models on GPUs in the cloud, 3. Create deep learning models using CNNs and LSTMs for biomedical signal data, 4. Create machine learning models for biomedical signal data, 5. Apply advanced signal pre-processing techniques for automated feature extraction, 6. Automatically generate code for edge deployment of AI models.
No installation of MATLAB is necessary. Please bring your laptop to the session.

Kathi Kugler, The MathWorks 

MATLAB DEVS – Discrete Event Simulation

MATLAB: MATLAB DEVS – Discrete Event and Hybrid Simulation
If you are interested in finding out how a well defined formalism for discrete event systems is made accessible for engineers and is extended by continuous systems integration, this tutorial is for you. As we use M&S as engineers, the underlying methodologies and algorithms often remain unexplained and vague.
The tutorial introduces the hyPDEVS Toolbox for MATLAB (former MatlabDEVS Tbx) which is based on the Parallel DEVS (PDEVS) extension of the Discrete EVent System Specification (DEVS) formalism and its associated abstract simulator algorithms. After providing a general understanding of how DEVS algorithms and DEVS modeling works, participants are guided through the process of modeling and simulation of a hybrid system.
Ideally, bring your laptop with MATLAB installed to the session, but joining without is possible, too. The TBX can be loaded from

Christina Deatcu, Univ. Wismar

Modelica  Thermofluid Stream Library

Are you interested in the efficient simulation of thermal architectures such as battery cooling for electric cars or reversible heat-pumps for building physics? Then this tutorial is for you. It provides an introduction into the DLR Thermofluid Stream Library, a free and open-source Modelica package:
We explain the underlying methodology that enables the unique robustness of this approach, we present simple examples to follow by yourself, demonstrate the scalability of the approach to complex applications and perform a small hands-on optimization exercise. You can follow the tutorial without equipment but having a laptop with OpenModelica or Dymola installed will enable you to take more out of it.

Dirk Zimmer, DLR 

Modelica System Dynamics Library

System Dynamics (SD) is a modeling technique that is mostly used in non-technical applications like economy or ecology. Several commercial simulation environments are available, but using an open source library, it can be used in Modelica and combined with other modeling paradigms.
The tutorial presents some basic SD applications and shows, how to implement them in a Modelica tool. It addresses SD experts, who want to use SD in Modelica, Modelica enthusiasts, who are interested in another modeling technique, and complete newbies. If time allows, we could also have a quick look into the internals of the open-source library.

Peter Junglas, PHWT Vechta

Agent-based Modelling for Health Sciences

Agent based applications are simulation techniques for real-world problems with eponymous agents as decision-making entities. Each of them evaluates its situation and chooses the next action according to a set of rules. Communication between agents leads to complex behavior patterns and provides valuable information about the dynamics of the real-world problems it emulates.
This tutorial provides a more detailed look at data processes in healthcare systems on the one hand, and COVID-19 scenario calculation using agent-based models on the other.

Dominik Rothschedl, Jakob Rosenberger, dwh Wien & TU Wien

Reinforcement Learning with Applications

Reinforcement Learning (RL), as a branch of artificial intelligence, provides a framework to imitate a natural, evolutionary learning process of agents in complex environments. It can lead to complex behavior patterns (policies) which are difficult to describe in a rule-based way. Significant advances have been made in applying deep learning methods to certain RL algorithms to reduce the computational cost of these methods.
This tutorial gives an introduction of basic algorithms (Q-Learning, Dyna) and leads to more complex applications in an agent-based simulation context, such as predator-prey models and finding policies in pathfinding.

Matthias Wastian, Dominik Brunmeir, dwh Wien & TU Wien