Virtual  /  December 13, 2021  -  December 14, 2021, 10:00 - 17:00

Data analytics for engineering data using machine learning

Unfortunately, the workshop is fully booked. We will gladly put you on the waiting list after your registration.

This two-day online workshop addresses the preparation, analysis and interpretation of numerical simulation data by machine learning methods. Besides the introduction of the most important concepts like clustering, dimensionality reduction, visualization and prediction, this course provides several practical hands-on tutorials using the python libraries numpy, scikit-learn and pytorch as well as the SCAI SimExplore.

Learning Outcomes

  • Basic knowledge on important machine learning methods to analyze numerical simulation data
  • Practical experience in applying these methods

Target audience

  • Researcher, developer and industrial end-users interested in new ways to analyze and visualize numerical simulation data


Day 1: December 13, 2021

09:45-10:00 Drop in to the videoconference
10:00-13:00 Introduction to machine learning methods like clustering and dimensionality reduction by means of short practical exercises in python
13:00-14:00 Lunch break
14:00-17:00 Application of the methods from the previous session to numerical simulation data stemming from engineering applications with the help of the SCAI SimExplore


Day 2: December 14, 2021

09:45-10:00 Drop in to the videoconference
10:00-13:00 Introduction to prediction by deep learning methods together with hands-on exercises using the software library pyTorch
13:00-14:00 Lunch break
14:00-17:00 Introduction to interpretability of machine learning methods with the help of the examples from the previous session



  • Preliminary experience with Python is required. Since Python is used, this tutorial can be used to learn the syntax
  • Preliminary experience in using Jupyter Notebook is also required