course-details-portlet

KP8105 - Mathematical Modelling and Model Fitting

About

Examination arrangement

Examination arrangement: Written examination and Work
Grade: Passed/Failed

Evaluation form Weighting Duration Examination aids Grade deviation
Approved exercises 25/100
Written examination 75/100 4 hours A

Course content

The course is given each second year, next time autumn 2021.
The course will cover:
Review of statistical methods.
Mathematical models:
Empirical models
Models based cause and effect relations.
Model fitting
Linear models
Non linear models
Model discrimination
Design of experiments
Response surface design
Design for non-linear models
Compulsory computer exercises and projects are part of the course.

Learning outcome

Knowledge:
The students will get knowledge about two different paths to modelling, empirical and physical. They will obtain knowledge about applied numerical methods, such as steepest descend, Newton iteration, numerical integration of ordinary differential equations, in addition to optimization methods.

Skills:
By completing the couse, the students are able to do linear and non-linear regression. They will be able to fit model parameters to measurements and estimate the confidence interval of parameters and model predictions. The students will be able to fit multi-response models to experimental data. They will be able to develop dynamic and steady-state models that will be used for explaining experimantal data. They will be able to do regression when there measurement errors in all variables, both in design and response variables. The students should be able to apply methods for model discrimination. Moreover, they should be able to apply methods for doing experimental design.

General competence:
The students will gain competance in using tools for model fitting and programming in matlab and/or python.

Compulsory assignments

  • Exercises

Specific conditions

Exam registration requires that class registration is approved in the same semester. Compulsory activities from previous semester may be approved by the department.

Required previous knowledge

Elementary knowledge in statistics, numerical methods, linear
algebra and computer programming.

Course materials

Handouts

Credit reductions

Course code Reduction From To
DIK2093 7.5
More on the course
Facts

Version: 1
Credits:  7.5 SP
Study level: Doctoral degree level

Coursework

Term no.: 1
Teaching semester:  AUTUMN 2020

No.of lecture hours: 3
Lab hours: 3
No.of specialization hours: 6

Language of instruction: English

Location: Trondheim

Subject area(s)
  • Technological subjects
Contact information
Course coordinator: Lecturer(s):

Department with academic responsibility
Department of Chemical Engineering

Phone:

Examination

Examination arrangement: Written examination and Work

Term Status code Evaluation form Weighting Examination aids Date Time Digital exam Room *
Autumn ORD Approved exercises 25/100
Room Building Number of candidates
Spring ORD Approved exercises 25/100
Room Building Number of candidates
Autumn ORD Written examination 75/100 A
Room Building Number of candidates
Spring ORD Written examination 75/100 A
Room Building Number of candidates
  • * The location (room) for a written examination is published 3 days before examination date. If more than one room is listed, you will find your room at Studentweb.
Examination

For more information regarding registration for examination and examination procedures, see "Innsida - Exams"

More on examinations at NTNU