TSRT92 · 6 credits · Advanced level (A1N) · Elektroteknik
Modelling and Learning for Dynamical Systems
Active · next start HT 2026
72%
passed the latest regular sitting
DAT1 · 30 Oct 2025 · 58 students
73%
passed the regular sittings, all sittings over the last 5 years
DAT1 · 261 students
75%
Harder than 75% of courses at advanced level
Compared with 285 courses at the same level, ordinary exams over the last three academic years. 100 students.
Part of 10 programmes
- Master of Science in Applied Physics and Electrical Engineering
- Master of Science in Chemical Biology
- Master of Science in Engineering Biology
- Master of Science in Computer Science and Engineering
- Master of Science in Industrial Engineering and Management
- Master of Science in Information Technology
- Master of Science in Computer Science and Software Engineering
- Master of Science in Mechanical Engineering
- Master of Science in Engineering Mathematics
- Master's Programme in Data Science and Information Engineering
Other module
DAT1 Computer Examination
- 5
- 4
- 3
- U
Main rounds
73% passed · 261 students
- 72%58
- 83%42
- 49%57
- 76%42
- 85%62
Later rounds
75% passed · 55 students
- 86%7
- 83%6
- 88%17
- 78%9
- 83%6
- 30%10
- Pass a later round: 75% (55 students), compared with 73% (261 students) on the ordinary exam
- Get 4 or 5: 34% (316 students)
Show small sittings (4)
Sittings with fewer than 5 students, often credit transfers or special sittings. They are not part of any figure on this page.
- 67%3
- 100%1
- 75%4
- 33%3
Other modules
Partial exams, hand-ins and labs. They are not part of the figures at the top.
Hand-in or lab
LAB1 Laboratory Work
- G
Main rounds
100% passed · 187 students
- 100%39
- 100%46
- 100%47
- 100%55
Later rounds
100% passed · 39 students
- 100%9
- 100%14
- 100%10
- 100%6
- Pass a later round: 100% (39 students), compared with 100% (187 students) on the ordinary exam
Show small sittings (15)
Sittings with fewer than 5 students, often credit transfers or special sittings. They are not part of any figure on this page.
- 100%2
- 100%1
- 100%2
- 100%2
- 100%4
- 100%2
- 100%2
- 100%2
- 100%1
- 100%1
- 100%2
- 100%1
- 100%1
- 100%2
- 100%2
About the course
Aim
The course should give knowledge about methods and principles for mathematical modeling of dynamical systems and how properties of the models can be studied through simulation. After completing the course, each student is expected to know how to:
- Use systematic methods to model various dynamical systems using physical principles and to analyse, simplify and simulate the derived models
- Estimate a model of a dynamical system by making suitable choices concerning experimental design, post-processing of data, model structure, and estimation method and carry out a thorough validation of the resulting model
- Analyse properties of system identification methods
Content
Models and modeling: Different types of models. Continuous and discrete time models. Differential and difference equations. State-space descriptions. Principles for model building, starting from physical relations. Balance and state equations. Simplification of models. Differential algebraic models. Object-oriented modeling. Models with disturbances. Black-box models. Neural networks.
Identification: Transient-response, frequency, correlation, and spectral analysis. Parameter estimation for linear and nonlinear dynamic models. Regularization. Classification. System identification as a model building tool. Model validation.
Simulation: Methods for state-space and differential algebraic models. The simulation languages Simulink and Modelica.
- Examiner
- Martin Enqvist
- Scheduled hours
- 58 h
- Main field of study
- Elektroteknik