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SM2001 Data-driven Methods in Engineering Mechanics 7.5 credits

Administer About course

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 ofdamage 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 machinelearning and deep learning. The course covers a variety of modeling and learning methodologies and algorithms, such as modern neural-network architectures, modaldecompositions, 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 modelingtools to solid and fluid mechanics, dynamical systems, control and more applied engineeringsettings (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 toprepare students with the tools and knowledge to use the techniques to conduct novel data-driven engineering, research and development.

Choose semester and course offering

Choose semester and course offering to see current information and more about the course, such as course syllabus, study period, and application information.

Application

For course offering

Autumn 2024 Start 26 Aug 2024 programme students

Application code

51835

Headings with content from the Course syllabus SM2001 (Autumn 2022–) 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 passing the course, the student should be able to:

  • Describe the meaning and significance of mathematical operations required to process, represent, and approximate data.
  • Describe the objectives, advantages, and disadvantages of various data-driven modeling techniques.
  • Describe how to load and manipulate large datasets in Matlab and/or Python.
  • Assess the required skills to apply various data-driven algorithms to potentially large and complex datasets
  • Analyze the results of modeling algorithms to build an enhanced understanding of a given dataset.
  • Describe and understand the physics of the underlying system that the data comes from.
  • Analyze the implications of the developed data-driven solutions for sustainable development.

Literature and preparations

Specific prerequisites

Completed degree project on Bachelor level with major in technology.

Recommended prerequisites

Basic course in computer science/programming (e.g. DD1337)

Basic course in linear algebra (e.g. SF1672 )

Equipment

Students may use their own laptop for the practical parts

Literature

Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control

Book by J. Nathan Kutz and Steven L. Brunton

Examination and completion

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

Grading scale

A, B, C, D, E, FX, F

Examination

  • PRO1 - Project, 4.5 credits, grading scale: A, B, C, D, E, FX, F
  • ÖVN1 - Exercises, 3.0 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

Students can improve their grade via a written exam

Opportunity to raise an approved grade via renewed examination

It is possible to improve the grade via a re-exam

Examiner

Profile picture Ricardo Vinuesa Motilva

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

SCI/Engineering Mechanics

Main field of study

Mechanical Engineering

Education cycle

Second cycle

Add-on studies

No information inserted

Contact

Ricardo Vinuesa Motilva (rvinuesa@kth.se)