Coursera’s Online Course on Statistical Mechanics

Sponsored Links

In the line-up of courses, ENS (École normale supérieure) has included a new online course named “Statistical Mechanics: Algorithms and Computations” in collaboration with Coursera.

The discussion of implementation of details will be kept at minimum during this course. It is completely self-contained. The course deeply relies on summarized algorithms. Components of this course will lead from basic discussions to rich and complex problems in modern-day physics. The contemporary physics is a topic of great interest to a wide range of students in the natural sciences.

This course is all about computational approach in modern Physics in a clear and accessible way. This course also includes the in-depth discussions of algorithms comprising the basic component methods to cutting-edge Markov-chain techniques. Here, applications in classical and quantum physics will be emphasized. This course will help the students in learning the subjects like molecular dynamics, transition phases in hard-sphere liquids, simulated annealing for solving geometrical constraints, Monte Carlo sampling, Bose-Einstein condensation and many-body physics with fermions.

Duration of Course

The session will start from February 3, 2014 for the duration of 10 weeks. The course will demand 4-6 hours/week for study. The course will be taught in English followed by its subtitles.

Eligibility

Participants are expected to have a basic knowledge in linear algebra and calculus. In addition they must be familiar with the chemistry or physics of the collegiate level. During this course students will be able to acquire expertise in Python language.

Course Format

This course will incorporate of videotaped lectures included nine homework assignments for ten weeks. Mini-Quizzes and assignments will also be integrated in the lectures. Final grades will be based on homework solutions (50%) and a multiple-choice exam (50%) at the end of the course.

Course Syllabus

Weeks and Themes:
-Introduction to Monte Carlo algorithms
-Hard disks: from Classical Mechanics to Statistical Mechanics
-Energy, free energy, and phase transitions
-Sampling and integration: From Gaussians to the Maxwell distribution.
-Density matrix and path integral 1/2
-Density matrix and path integral 2/2
-Bose-Einstein condensation
-Classical spin models: From basic enumerations to the Cluster Monte Carlo algorithms
-Classical spin models: “faster-than-the” clock algorithm
-The alpha and omega of Monte Carlo algorithms: Achieving zero variance (for Buffon’s needles) and coping with infinite variances (Paul Lévy’s stable distributions)

About the Instructor

Werner Krauth

He is a theoretical physicist, CNRS research director, Director of the Physics Department at Ecole normale supérieure.

, , ,

Sponsored Links
Need Scholarship Help? Comment and Discuss.