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.

Location

Online course
Organizer: HLRS, University of Stuttgart, Germany

Start date

Jan 20, 2022
10:00

End date

Jan 21, 2022
17:00

Language

English

Entry level

Basic

Course subject areas

Data in HPC / Deep Learning / Machine Learning

Topics

Artificial Intelligence

Big Data

Deep Learning

Machine Learning

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