Table of contents

The course is connected to the following study programs

Teaching language

English

Learning outcomes

On successful completion of the course, the student should be able to

  • design experiments and interpret experimental data within a physics-based framework
  • pre-process data sets, including cleaning, filtering and clustering
  • apply different regression and interpolation approaches to unveil data relationships
  • establish and solve optimization problems
  • perform numerical differentiation and integration of data sets
  • solve numerically ordinary and partial differential equations

use software packages with powerful libraries (like Matlab) to perform R&D activities

Contents

  • Dimensional analysis: Buckinghams P theorem, relevant dimensionless quantities/numbers
  • Similarity theory: notion of scale, similarity and types of similarity
  • Data mining: outliers, linear and nonlinear correlation and PCA
  • Signal processing: FFT and data filtering
  • Regression and interpolation: linear and nonlinear regression, machine learning regressors, polynomial and stepwise interpolation
  • Optimization: linear and nonlinear programming, multiobjective optimization, global optimization
  • Numerical integration and differentiation: Newton-Cotes formulas, Romberg integration, adaptive quadrature methods, use of Taylor series and derivatives of data
  • Ordinary differential equations: Runge-Kutta methods, initial value problems, boundary value problems

  • Partial differential equations: finite difference methods, elliptic, parabolic and hyperbolic PDEs

Teaching and learning methods

Lectures, exercises and laboratory work. Estimated work load for the average student is approximately 200 hours.

Examination requirements

Satisfactory submission of compulsory exercises/projects done in group. Information will be given in the Canvas at the beginning of the course.

Examinations

Portofolio. Group graded assessment. The group as a whole is graded. Further information about contents and weighting of the exercises/projects will be given in Canvas at the beginning of the semester.

Student evaluation

The person responsible for the course decides, in cooperation with student representative, the form of student evaluation and whether the course is to have a midway or end of course evaluation in accordance with the quality system for education, chapter 4.1.”

Offered as a free-standing course

Yes, if there are places available.

Admission Requirement if given as a free-standing course

Admission requirements for the course are the same as for the master’s programme in Renewable energy.
Last updated from FS (Felles studentsystem) Oct. 15, 2026 8:20:39 AM