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.

As part of the EXCELLERAT P2 training program, Fraunhofer SCAI in cooperation with HLRS offers a three-day workshop on data analytics for simulation data using machine learning.

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

Jun 12, 2023

End date

Jun 14, 2023



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)


CEST time:

Day 1: 12 June 2023

  • 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: 13 June 2023

  • 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: 14 June 2023

  • 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/ML4SIM4 course are already available on the EXCELLERAT Portal.

Updated exercises and slides will be made available during the course.

Registration information

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

Registration closes on May 26, 2023.


  • 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 Training Collaborations in HPC

HLRS is part of the Gauss Centre for Supercomputing (GCS), together with JSC in Jülich and LRZ in Garching near Munich. EuroCC@GCS is the German National Competence Centre (NCC) for High-Performance Computing. HLRS is also a member of the Baden-Württemberg initiative bwHPC.


This course is partly realised in cooperation with the Centre of Excellence EXCELLERAT P2 (funded by the European Union, grant agreement No 101092621). See also the EXCELLERAT Service Portal for more information.

Further courses

See the training overview and the Supercomputing Academy pages.

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