Course content

This course aims at providing students experience about processing of engineering data and assessment of the performance of complex technical and socio-technical systems. The course relies on computational experiments performed with the scripting language Python and the associated data processing packages (e.g.numpy, panda, and matplotlib).

The course is organized around 4 themes:

Theme 1: Processing of engineering data.

Theme 2: Definition and evaluation of performance indicators.

Theme 3: Automated exploration of design solution spaces.

Theme 4: Use of machine learning techniques for performance optimization.

Learning outcome

Knowledge:

The students should have advanced knowledge on processing of engineering data and assessment of performance indicators of complex technical and socio-technical systems. The students should be able to change formats of data, analyze data, chain engineering tools, and perform specific calculations and simulations.

Skills:

The students shall be able to write large scripts to handle data in various format, to chain automatically the execution of tools, and to perform calculations of performance indicators. They shall be able to structure these scripts in a way they can be maintained and reused.

General competence:

The students shall master a scripting language and use it as a productivity tool in their engineering work. They shall master also the definition and the assessment of performance indicators. They shall be able to report results of experiments in concise and yet complete and informative way.

Learning methods and activities

The course is mainly based on computational experiments. Students are provided with program code that will be modified and extended. The programs will be used for experiements and simulations, and reports with the results shall be sumbitted.

Specific conditions

Recommended previous knowledge

TPK4100 Operations Management, TPK4115 Project Planning and Control

TDT4105/TDT4110 Information Technology Introduction. Basic skills in Matlab or Python.

Course materials

Antoine Rauzy. Performance Engineering in Python. AltaRica Association. 2020. ISBN 978-82-692273-1-4. pdf available on author's webpages.

Guttag, John. Introduction to Computation and Programming Using Python: With Application to Understanding Data. 2nd ed. MIT Press, 2016. ISBN: 9780262529624. Wes McKinney.

Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython. O'Reilly Media; 2 edition, 2017, ISBN-13: 978-1491957660 Computer programs presented during the lectures and tutorials.