NFYA04034U Inverse Problems

Volume 2020/2021
Education

MSc Programme in Physics

MSc Programme in Physics with a minor subject

Content

Inverse problems are problems where physical data from indirect measurements are used to infer information about unknown parameters of physical systems. Noise‐contaminated data and prior information on model parameters are the basic elements of any inverse problem. Using probability theory, we seek a consistent formulation of inverse problems, and from our fully probabilistic results we can, in principle, answer any question pertaining our state of information about the system when all information has been integrated. The objective of the course is to provide theory and methods for solving and analyzing inverse problems. A significant part of the course involves work with projects where inverse problems from physicical disciplines will be analyzed.

Learning Outcome

Skills
This course aims to provide the student with skills to

  • Describe and quantify data uncertainties and modeling errors.

  • Describe available prior (external) information using probabilistic/​statistical models and methods

  • Solve inverse problems

    • Linear and weakly non-linear Gaussian inverse problems

      • Probabilistic least squares inversion

      • Classical parameter estimation methods and regularization

    • Non-linear non-Gaussian inverse problem

      • Importance sampling (rejection, Metropolis, extended Metropolis)

  • Analyze and validate solutions to inverse problems

Knowledge
This course will give the student a mathematical description of inverse problems as they appear in connection with measurements and experiments in physical sciences. It teaches them to solve linear inverse problems with analytical and numerical methods and non-linear problems with Monte Carlo methods. The students will study the propagation of noise in data to uncertainty in the solutions.

Competences
Through the course the student will be able to identify inverse problems in various fields of physical sciences, classify them, and choose appropriate solution methods. The student will be able to treat data uncertainties and to evaluate the accuracy and resolution of the inverse solution.

See Absalon for final course material. The following is an example of expected course litterature.

 

Tarantola (2005) Inverse Problem Theory, and Lecture notes.

Throughout the course Matlab will be used extensively, and therefore an introductory programming course in MatLab is recommended.
Knowledge of Linear Algebra corresponding to the B.Sc. in physics or mathematics is expected.

Academic qualifications equivalent to a BSc degree is recommended.
Lectures, exercises (using Matlab), and projects.
  • Category
  • Hours
  • Lectures
  • 27
  • Preparation
  • 73
  • Practical exercises
  • 16
  • Project work
  • 50
  • Guidance
  • 40
  • Total
  • 206
Oral
Credit
7,5 ECTS
Type of assessment
Continuous assessment
Oral examination, 20 minutes
3 projects (group or individual) [weighed by 12.5%, 12.5% and 25%] followed by 1 individual oral examination [weighed by 50%]. Both the continuous evaluation and the oral examintation should be pased separately.
Marking scale
7-point grading scale
Censorship form
No external censorship
Several internal examiners
Re-exam

Same as ordinary exam. The student can choose to re-use points from projects handed in during the course, or make new projects, which must be handed in no later than 2 weeks before the oral re-exam.

Criteria for exam assesment

see "learning outcome"