Data analytics for engineering data using machine learning

Crash Bifurcation. Source: Victor Rodrigo Iza-Teran, Copyright Fraunhofer SCAI
This course will be held online with Zoom.

Fraunhofer SCAI in cooperation with HLRS offers a three-day workshop on data analytics for simulation data using machine learning. The content of this workshop was developed within the training program of EXCELLERAT.

This three-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 DataViewer (see also the SimExplore tool).

Learning outcomes

  • Basic knowledge on important machine learning methods to analyze numerical simulation data.
  • Moreover, practical experience in applying these methods.

Target audience

Researchers, developers and industrial end users interested in new ways to analyze and visualize numerical simulation data.


Online course
Organizer: HLRS, University of Stuttgart, Germany

Start date

Nov 21, 2022

End date

Nov 23, 2022



Entry level


Course subject areas

Data in HPC / Deep Learning / Machine Learning


Artificial Intelligence

Big Data

Deep Learning

Machine Learning

Scientific Machine Learning

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Prerequisites and content levels

  • Preliminary experience with Python is required. Since Python is used, the following tutorial can be used to learn the syntax.
  • Preliminary experience in using Jupyter Notebook is also required.
Content levels
  • Beginners' level: 4 hours
  • Intermediate level: 5 hours
  • Community level: 5 hours

Learn more about course curricula and content levels.


Bastian Bohn, Christian Gscheidle and Sara Hahner (Fraunhofer SCAI)


CET time:

Day 1: 21 November 2022

  • 08:45-09:00 Drop in to the videoconference
  • 09:00-12:30 Introduction to machine learning methods like clustering and dimensionality reduction by means of short practical exercises in python
  • 12:30-13:30 Lunch break
  • 13:30-17:00 Application of the methods from the previous session to numerical simulation data stemming from engineering applications with the help of the SCAI DataViewer

Day 2: 22 November 2022

  • 08:45-09:00 Drop in to the videoconference
  • 09:00-12:30 Introduction to prediction by deep learning methods together with hands-on exercises using the software library pyTorch

Day 3: 23 November 2022

  • 08:45-09:00 Drop in to the videoconference
  • 09:00-12:30 Introduction to interpretability of machine learning methods with the help of the examples from the previous session


Notebooks and data of the equivalent 2022/ML4SIM2 course are available on the EXCELLERAT portal.

Registration information

Register at Fraunhofer via the button at the top of this page.

Registration closes on November 4, 2022.


  • Students without master’s degree or equivalent: 300 EUR
  • PhD students or employees at a German university or public research institute: 300 EUR
  • PhD students or employees at a university or public research institute in an EU, EU-associated or PRACE country other than Germany: 300 EUR
  • PhD students or employees at a university or public research institute outside of EU, EU-associated or PRACE countries: 600 EUR
  • Other participants, e.g., from industry, other public service providers, or government: 600 EUR

Link to the EU and EU-associated (Horizon Europe), and PRACE countries.


Lorenzo Zanon, phone 0711 685 63824, zanon(at)


HLRS is part of the Gauss Centre for Supercomputing (GCS), which is one of the six PRACE Advanced Training Centres (PATCs) that started in Feb. 2012.

This course is not part of the PATC curriculum and is not sponsored by the PATC program.

HLRS is also member of the Baden-Württemberg initiative bwHPC.

Official course URL(s), and course website at Fraunhofer SCAI

Further courses

See the training overview and the Supercomputing Academy pages.

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