Faculty of Science

The Physics of Algorithms PhD course
Provider: Faculty of Science

Activity no.: 5860-18-11-31
	Enrollment deadline: 13/02/2018

Place

Niels Bohr Institute

Date and time

14.02.2018, at: 00:00 - 23.04.2018, at: 16:00

Regular seats

ECTS credits

7.50

Contact person

Julie Meier E-mail address: juliemh@nbi.ku.dk

Enrolment Handling/Course Organiser

Bjarne Bøgeskov Andresen E-mail address: andresen@nbi.ku.dk

Written language

English

Teaching language

English

Semester/Block

Block 3

Scheme group

Exam form

Oral examination: 30 min

Exam form

Written assignment

Exam details

Each student has a choice between two forms of exam: - a traditional oral exam without preparation time. - a term report of max. 15 pages about a personal project agreed on between the student and teacher. The date for the examination and the due date of the report are the same

Exam aids

All aids allowed

Grading scale

Passed / Not passed

Criteria for exam assessment

See Skills

Course workload

Course workload category	Hours
Lectures	30.00
Theory exercises	16.00
Theory exercises	0.50
Preparation	159.50

Sum	206.00

Content

To provide the students with a toolbox of optimization and modeling algorithms along with a sense of which ones work best in a given situation
To inspire the students to make use of analogies between physics and optimization and develop new ones

Recent methods are covered where physics has contributed significantly to the understanding and development of algorithms. Examples include Monte Carlo type algorithms like simulated annealing and genetic algorithms as well as maximum entropy solutions, information theory, and neural nets. A number of such algorithms will be presented theoretically as well as in practice, and the connections between physics and optimization will be emphasized. Students will get hands-on experience with implementing the methods during the exercise sessions. Students are expected to put serious effort into these implementations.

Learning outcome

Skills:
The students have completed the course in full when they can:

- Identify analogies between physical phenomena and optimization
- Select and use optimizations methods for a particular problem and argue for their choice
- Identify optimization opportunities in their own field of research

Knowledge:
Through this course the student will learn about modeling algorithms, optimization, Monte Carlo calculations, information theory, neural nets, a.o. Emphasis will be on understanding the relationship between physics and optimization. The students will also learn that many traditional physics laws are really optimized outcomes of particular objective functions.

Competences:
The student will at the end of this course be able to understand algorithms and optimization, see their relation to physics, and especially use these techniques within their own field of research.

Literature

Course notes and excerpts from articles and books are available on the course webpage for registered students.

Teaching and learning methods

Mixture of lectures and exercises.

Remarks

Academic qualifications:
Contents of the first year of the physics bachelor program including supporting courses.

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