Data analytics for engineering data using machine learning

This course will be held online with Zoom.

This course is a second instance of the same course provided on December 13-14, 2021 (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

20. Jan 2022
10:00

Verstaltungsende

21. Jan 2022
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: 20 January 2022

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: 21 January 2022

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 of the equivalent 2021/ML4SIM course 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.

Official course URL(s)

http://www.hlrs.de/training/2022/ML4SIM

Further courses

http://www.hlrs.de/training/ and http://www.hlrs.de/training/overview/

Höchstleistungsrechenzentrum Stuttgart

Data analytics for engineering data using machine learning

Veranstaltungsort

Prerequisites and content levels

Prerequisites

Content levels

Instructors

Agenda

Handout

Fees

Contact

PRACE PATC and bwHPC

EXCELLERAT

Official course URL(s)

Further courses

Ähnliche Trainingskurse

Supercomputing Akademie: Datenanalyse mit HPC

Data analytics for engineering data using machine learning