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Syllabus for

Academic year
TIF150 - Information theory for complex systems
 
Syllabus adopted 2012-02-22 by Head of Programme (or corresponding)
Owner: MPCAS
7,5 Credits
Grading: TH - Five, Four, Three, Not passed
Education cycle: Second-cycle
Major subject: Bioengineering, Chemical Engineering, Engineering Physics
Department: 16 - PHYSICS


Teaching language: English
Open for exchange students
Block schedule: B

Course module   Credit distribution   Examination dates
Sp1 Sp2 Sp3 Sp4 Summer course No Sp
0107 Examination 7,5 c Grading: TH   7,5 c   11 Mar 2013 am M,  22 Aug 2013 am V

In programs

MPCAS COMPLEX ADAPTIVE SYSTEMS, MSC PROGR, Year 1 (elective)

Examiner:

Professor  Kristian Lindgren


Course evaluation:

http://document.chalmers.se/doc/79191b98-df40-4555-a4e5-2ff9b4138d04


Eligibility:

For single subject courses within Chalmers programmes the same eligibility requirements apply, as to the programme(s) that the course is part of.

Course specific prerequisites

Basic undergraduate mathematics and probability theory.

Aim

The course introduces the students to important concepts in information theory that can be used to describe and characterise complex systems. The concepts are applied to a number of areas in complex systems: cellular automata, fractals, chemical self-organisation, and chaos. The main aim is to give students the knowledge and skills to apply information theory to a wide variety of different systems. The course also gives a presentation of the connections between information theory and physics, primarily statistical mechanics and thermodynamics.

Learning outcomes (after completion of the course the student should be able to)

After successfully completing this course the students will be able to

Define and use the basic concepts of information theory: Shannon entropy, relative entropy, complexity measures based on these
Use information theory to characterise both cellular automata and low-dimensional chaos
Understand the connection between information theory and statistical mechanics
Use geometric information theory to characterise patterns in spatically extended systems like pictures
Explain how information is flowing in chemical self-organising systems exhibiting pattern formation

Content

Basic concepts of information theory
- Shannon entropy, relative information, complexity measures
Information theory for symbol sequences and lattice systems
- correlations and randomness in symbol sequences
Information theory and physics
- entropy in physics and its relation to randomness in information theory
Cellular automata
- order and disorder in the time evolution of various cellular automaton rules
Geometric information theory
- presentation of an information-theoretic framework for characterising patterns
Self-organising chemical systems
- flows of information in the process of pattern formation
Chaotic systems and information
- flows of information from micro to macro in chaotic systems

Organisation

The course is based on a series of lectures, in total 30 hours, covering the topics listed above. Every week a set of home assignments are distributed.

Literature

K. Lindgren, Information theory for complex systems. Lecture notes, Chalmers, 2008

Examination

The examination will be based homework assignments (10%) and a final exam (90%).


Page manager Published: Thu 03 Nov 2022.