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PhD course in Data-driven Methods in Engineering

August 28 @ 15:00 - October 27 @ 17:00

CREDITS 7.5 credits
EXAMINER Ricardo Vinuesa, KTH Royal Institute of Technology
Registration Please register here.
PREREQUISITES A good understanding of standard topics in engineering mathematical analysis will be very helpful. In particular, a strong background in linear algebra, differential equations, and optimization will be beneficial. Since hands-on data-driven modeling will invariably require some coding, familiarity with Matlab, Python or other similar languages/platforms will be helpful.
AIM The course is aimed at methods for analyzing complex systems and models.The course should equip students with the ability to use different methods in industrial projects.
LEARNING OUTCOMES After taking the course the students should be able to:

  • Understand the meaning and significance of mathematical operations required to process, represent, and approximate data.
  • Understand the objectives, advantages, and disadvantages of various data-driven model- ing techniques.
  • Learn how to load and manipulate large datasets in Matlab and/or Python.
  • Develop the required skills to apply various data-driven algorithms to potentially large and complex datasets
  • Interpret the results of modeling algorithms to build an enhanced understanding of a given dataset.
  • Interpret and understand the physics of the underlying system that the data comes from.
  • Be able to assess the implications of the developed data-driven solutions for sutainable development.
  • Dimensionality Reduction (Part I):
    • This section introduces tools for finding low-dimensional representations of high-dimensional data, which allows for data to be efficiently stored, transferred, and analyzed.
  • Machine Learning and Data Analysis (Part II):
    • This section will give a relatively brief tour through aspects of data analysis, from classical curve fitting to neural networks and deep learning, building on the material introduced in Part I.
  • Dynamics, Control and Reduced-Order Models (Part III):
    • In this section, we assume that the data that we are studying comes from some underlying physical laws (in the context of dynamical systems, solid mechanics, fluid mechanics, etc.), which can be learned/approxi- mated from data, or from some combination of data and physics.
  • Final Project (Part IV):
    • The students will apply the techniques developed in this course (or extensions thereof) to a dataset/problem of their own choosing.
COURSE EVALUATION There are three parts contributing to the course grade:

  • Homework (2 hp). These will be solved individually and submitted for evaluation. The student will have two opportunities to implement the teacher’s feedback and obtain a passing grade.
  • Final exam (2 hp). The exam will last 3 hours and will contain both theoretical and practical aspects discussed in the course.
  • Project (3.5 hp). The students will propose research projects in groups of around 4. These projects will be developed throughout the semester with the input of the teachers. The evaluation will be based on a final report and presentation.

The final grade will be Pass/Fail, and in order to pass the course the student needs to pass the three parts.

LITERATURE Data-driven Science and Engineering: Brunton and Kutz. Cambridge University Press. The book is not required to follow the course.
EXAMINATION The main examination task is a written project report that can be aligned towards the PhD students’ research.The grade is passed/not passed.
DETAILED SCHEDULE The course consists of 15 theory lectures, 6 practical lectures, 1 presentation session and 1 final exam.
For schedule see the course webpage:


August 28 @ 15:00
October 27 @ 17:00


KTH Royal Institute of Technology
View Organiser Website