Höchstleistungsrechenzentrum Stuttgart: Data Analytics for Engineering Data using Machine Learning

Prerequisites and content levels

Prerequisites

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.

Instructors

Arno Feiden, Christian Gscheidle and Daniela Steffes-lai (Fraunhofer SCAI)

Agenda

CEST times:

Day 1: Monday, April 20, 2026

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: Tuesday, April 21, 2026

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: Wednesday, April 22, 2026

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

Handout

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

Registration information

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

Registration will close as soon as the course capacity is reached.

Fees

The course is open and free of charge for participants from academia, industry, and public administration from the Member States (MS) of the European Union (EU) and Associated/Other Countries to the Horizon Europe programme.

Contact

Junghwa Lee (HLRS), phone 0711 685 87228, training(at)hlrs.de

Christian Gscheidle (Fraunhofer SCAI), christian.gscheidle(at)scai.fraunhofer.de

Arno Feiden (Fraunhofer SCAI), arno.feiden(at)scai.fraunhofer.de

Official course URLs

http://www.hlrs.de/training/2026/ML4SIM, on the EXCELLERAT Portal (in progress), and course website at Fraunhofer SCAI.

Further courses and training team

See the training overview and the Supercomputing Academy pages.
See also information about the HLRS training department and staff.

HLRS Training Collaborations in HPC and AI

HLRS is part of the Gauss Centre for Supercomputing (GCS), together with JSC in Jülich and LRZ in Garching near Munich. SIDE is the German National Competence Centre (NCC) for High-Performance Computing. HLRS is also a member of the Baden-Württemberg initiative bwHPC.
Since 2025, HLRS has been coordinating one of the AI Factories of the EuroHPC JU: HammerHAI.

Acknowledgements

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.

This event is offered as part of HammerHAI, Germany’s first AI Factory, which has a dedicated focus on industry, manufacturing, engineering, and research. HammerHAI provides AI resources and solutions, an upcoming AI-optimized supercomputer, and personalized expert support for AI users at all stages in the AI lifecycle. This project has received funding from the European High Performance Computing Joint Undertaking under grant agreement No. 101234027. This project is co-funded by the European Commission, the German Federal Ministry of Research, Technology and Space (BMFTR), the Baden-Württemberg Ministry of Science, Research and the Arts, the Bavarian State Ministry of Science and the Arts and the Lower Saxony Ministry of Science and Culture.

Data Analytics for Engineering Data using Machine Learning

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