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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 Microsoft Teams.

We are happy to inform that another instance of this same course will be provided on January 20-21, 2022 (2021/ML4SIM).

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

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.
  • 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.

Veranstaltungsort

Online course
Organizer: HLRS, University of Stuttgart, Germany

Veranstaltungsbeginn

13. Dez 2021
10:00

Verstaltungsende

14. Dez 2021
17:00

Sprache

Englisch

Einstiegslevel

Basis

Themenbereiche

Daten in HPC / Deep Learning / Maschinelles Lernen

Themen

Künstliche Intelligenz

Big Data

Deep Learning

Maschinelles Lernen

Scientific Machine Learning

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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

Bastian Bohn, Christian Gscheidle and Moritz Wolter (Fraunhofer SCAI)

Agenda

CET time:

Day 1: 13 December 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: 14 December 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

Handout

Notebooks and data are available on the EXCELLERAT portal.

Fees

Students without Diploma/Master: 25 EUR
Students with Diploma/Master (PhD students) at German universities: 45 EUR
Members of German universities and public research institutes: 45 EUR
Members of universities and public research institutes within EU or PRACE member countries: 90 EUR
Members of other universities and public research institutes: 180 EUR
Others: 420 EUR

Contact

Lorenzo Zanon, phone 0711 685 63824, zanon(at)hlrs.de

PRACE PATC and bwHPC

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.

EXCELLERAT

This workshop is supported by the Horizon-2020 Centre of Excellence EXCELLERAT. See also the EXCELLERAT Service Portal for more information.

Ähnliche Trainingskurse

Alle Trainingskurse
Data analytics for engineering data using machine learning

März 10 - 12, 2025

Online

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