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FSM3001 Data-driven Methods in Engineering 7.5 credits

The  amount  of  data  generated  in  the  modern  engineering  solutions  is  immense,  and  it  will  only grow.    It  is  imperative  to  exploit  the  data  for  model  development,  continuous  evaluation  of damage  and  testing. This  graduate-level  course  focuses  on  state-of-the-art  techniques  in  data-driven  modeling.  The course  introduces  relevant  aspects  of  probability  theory,  optimization,  and  the  basics  of  machine learning  and  deep  learning.  The  course  covers  a  variety  of  modeling  and  learning methodologies  and  algorithms,  such  as  modern  neural-network  architectures,  modal decompositions,  identification  of  linear  and  nonlinear  dynamics,  and  other  advanced  topics  in data-driven  modeling.  The  emphasis  will  be  on  the  application  of  modern  data-driven  modeling tools  to  solid  and  fluid  mechanics,  dynamical  systems,  control  and  more  applied  engineering settings  (naval  and  vehicle  engineering,  aeronautics,  railways  and  wind  energy). The  course  will  finalize  by  a  project  where  students  apply  techniques  developed  in  this  course (or  extensions  thereof)  to  a  particular  dataset/problem. In  this  sense,  the  course  is  designed  to prepare  students  with  the  tools  and  knowledge  to  use  the  techniques  to  conduct  novel  datadriven  engineering,  research  and  development. 

About course offering

For course offering

Autumn 2024 Start 26 Aug 2024 programme students

Target group

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Part of programme

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P1 (7.5 hp)


26 Aug 2024
27 Oct 2024

Pace of study


Form of study

Normal Daytime

Language of instruction


Course location

KTH Campus

Number of places

Places are not limited

Planned modular schedule


For course offering

Autumn 2024 Start 26 Aug 2024 programme students

Application code



For course offering

Autumn 2024 Start 26 Aug 2024 programme students


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Course coordinator

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Headings with content from the Course syllabus FSM3001 (Autumn 2021–) are denoted with an asterisk ( )

Content and learning outcomes

Course contents

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/approximated 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.

Intended 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 modeling 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.

Literature and preparations

Specific 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.

Recommended prerequisites

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Examination and completion

If the course is discontinued, students may request to be examined during the following two academic years.

Grading scale

P, F


  • PRO1 - Project, 3.5 credits, grading scale: P, F
  • TEN1 - Written exam, 2.0 credits, grading scale: P, F
  • ÖVN1 - Home work, 2.0 credits, grading scale: P, F

Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.

The examiner may apply another examination format when re-examining individual students.

Opportunity to complete the requirements via supplementary examination

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Opportunity to raise an approved grade via renewed examination

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Ethical approach

  • All members of a group are responsible for the group's work.
  • In any assessment, every student shall honestly disclose any help received and sources used.
  • In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.

Further information

Course room in Canvas

Registered students find further information about the implementation of the course in the course room in Canvas. A link to the course room can be found under the tab Studies in the Personal menu at the start of the course.

Offered by

Main field of study

This course does not belong to any Main field of study.

Education cycle

Third cycle

Add-on studies

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Postgraduate course

Postgraduate courses at SCI/Engineering Mechanics