Diffusive and Stochastic Processes
Stochastic descriptions offer powerful ways to understand fluctuating and noisy phenomena, and are widely used in many disciplines including physics, chemistry, biology, and economics. In this course, basic analytical and numerical tools to analyze stochastic phenomena are introduced and will be demonstrated on several important examples. Students will learn to master stochastic descriptions for analyzing non-equilibrium complex phenomena.
MSc Programme in Physics
MSc Programme in Physics with a minor subject
At the conclusion of the course students are expected to be able to:
- Describe diffusion process using Langevin equation and Fokker-Plank equation.
- Solve several examples of the first passage time problems.
- Explain basic concepts in stochastic integrals, and use it to describe geometric Brownian motions.
- Explain the Poisson process and the birth and death process. Use master equations to describe time evolution and steady state of the processes.
- Explain the relationship between master equations and Fokker-Plank equations using approximation methods such as Kramers-Moyal expansions.
- Explain asymmetric simple exclusion process and related models to describe traffic flow and jamming transition in one-dimensional flows.
- Apply the concepts and techniques to various examples of stochastic phenomena.
In this course, the basic tools to analyse stochastic phenomena are introduced by using the diffusion process as one of the most useful examples of stochastic process. The topics include Langevin equations, Fokker-Planck equations, first passage problems, and master equations. The tools are then used to analyze selected stochastic models that have wide applications to various real phenomena. The topics are chosen from non-equilibrium stochastic phenomena, including geometric Brownian motion (used in e.g. modeling finance), birth and death process (used in e.g. chemical reactions and population dynamics), and asymmetric simple exclusion process (used in e.g. traffic jam formation). Throughout the course, exercises for analytical calculations and numerical simulations are provided to improve the students' skills.
This course will provide the students with mathematical tools that have application in range of fields within and beyond physics. Examples of the fields include non-equilibrium statistical physics, biophysics, soft-matter physics, complex systems, econophysics, social physics, chemistry, molecular biology, ecology, etc. This course will provide the students with a competent background for further studies within the research field, i.e. a M.Sc. project.
Lectures and exercise sessions. Computer exercise included.
Equilibrium statistical physics, physics bachelor level
mathematics (Especially: differential and integral calculus,
differential equations, Taylor expansions). Basic programming
Academic qualifications equivalent to a BSc degree is recommended.
It is expected that the student brings a laptop with a programming environment (e.g. Python) installed.
Written feedback to assignments
- 7,5 ECTS
- Type of assessment
Written assignment, Programming assignment, 8 daysOral examination, 25 minutes
- Type of assessment details
- 20% of the grade from a programming assignment given at least
two weeks before the final exam.
80% of the grade from a 25-minute oral examination with no aids allowed and no preparation time.
The two parts of the exam do not need to be passed separately.
- Only certain aids allowed
All aids allowed for the written assignment.
No aids allowed for the oral examination.
- Marking scale
- 7-point grading scale
- Censorship form
- No external censorship
Several internal examiners
Criteria for exam assessment
See Learning outcome
Single subject courses (day)
- Theory exercises
- Course number
- 7,5 ECTS
- Programme level
- Full Degree Master
- Block 4
- No restriction
The number of seats may be reduced in the late registration period
- Study Board of Physics, Chemistry and Nanoscience
- The Niels Bohr Institute
- Faculty of Science
- Namiko Mitarai (7-706c776475646c4371656c316e7831676e)
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Courseinformation of students