Statistical Mechanics: Algorithms and Computations
- 4.8
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Approx. 16 hours to complete
Course Summary
This course covers the principles of statistical mechanics and the thermodynamics of macroscopic systems. Learn about the behavior of atoms and molecules and how to apply statistical mechanics to various physical systems.Key Learning Points
- Understand the basic principles of statistical mechanics and thermodynamics
- Learn how to apply statistical mechanics to physical systems
- Explore the behavior of atoms and molecules
Job Positions & Salaries of people who have taken this course might have
- USA: $75,000 - $150,000
- India: ₹700,000 - ₹2,000,000
- Spain: €30,000 - €50,000
- USA: $75,000 - $150,000
- India: ₹700,000 - ₹2,000,000
- Spain: €30,000 - €50,000
- USA: $60,000 - $120,000
- India: ₹500,000 - ₹1,500,000
- Spain: €25,000 - €40,000
- USA: $75,000 - $150,000
- India: ₹700,000 - ₹2,000,000
- Spain: €30,000 - €50,000
- USA: $60,000 - $120,000
- India: ₹500,000 - ₹1,500,000
- Spain: €25,000 - €40,000
- USA: $80,000 - $150,000
- India: ₹600,000 - ₹2,000,000
- Spain: €30,000 - €50,000
Related Topics for further study
Learning Outcomes
- Understand the fundamental principles of statistical mechanics and thermodynamics
- Apply statistical mechanics to various physical systems
- Develop critical thinking skills in analyzing and solving problems related to statistical mechanics
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of calculus and physics
- Familiarity with probability theory
Course Difficulty Level
IntermediateCourse Format
- Online
- Self-paced
Similar Courses
- Quantum Mechanics for Everyone
- Introduction to Thermodynamics: Transferring Energy from Here to There
Related Education Paths
Notable People in This Field
- Richard Feynman
- Albert Einstein
Related Books
Description
In this course you will learn a whole lot of modern physics (classical and quantum) from basic computer programs that you will download, generalize, or write from scratch, discuss, and then hand in. Join in if you are curious (but not necessarily knowledgeable) about algorithms, and about the deep insights into science that you can obtain by the algorithmic approach.
Outline
- Monte Carlo algorithms (Direct sampling, Markov-chain sampling)
- Lecture 1: Introduction to Monte Carlo algorithms
- Tutorial 1: Exponential convergence and the 3x3 pebble game
- Homework Session 1: From the one-half rule to the bunching method
- Python programs and references
- Errata (Lecture 1)
- Practice quiz 1: spotting a correct algorithm
- Hard disks: From Classical Mechanics to Statistical Mechanics
- Lecture 2: Hard disks: from Classical Mechanics to Statistical Mechanics
- Tutorial 2: Equiprobability, partition functions, and virial expansions for hard disks
- Homework Session 2: Paradoxes of hard-disk simulations in a box
- Python programs and references
- Practice quiz 2: spotting a correct algorithm (continued)
- Entropic interactions and phase transitions
- Lecture 3: Entropic interactions, phase transitions
- Tutorial 3: Algorithms, exact solutions, thermodynamic limit
- Homework Session 3: Two-dimensional liquids and solids
- Python programs and references
- Errata (Tutorial 3)
- Practice quiz 3: Spotting a correct algorithm (continued)
- Sampling and integration
- Lecture 4: Sampling and Integration - From Gaussians to the Maxwell and Boltzmann distributions
- Tutorial 4: Sampling discrete and one-dimensional distributions
- Homework Session 4: Sampling and integration in high dimensions
- Python programs and references
- Practice quiz 4: four disks in a box
- Density matrices and Path integrals (Quantum Statistical mechanics 1/3)
- Lecture 5: Density matrices and path integrals
- Tutorial 5: Trotter decomposition and quantum time-evolution
- Homework session 5: Quantum statistical mechanics and Quantum Monte Carlo
- Python programs and references
- Practice quiz 5: Four disks in a box (continued)
- Lévy Quantum Paths (Quantum Statistical mechanics 2/3)
- Lecture 6: Lévy sampling of quantum paths
- Tutorial 6: Bosonic statistics (with wave functions)
- Homework session 6: Path sampling: A firework of algorithms
- Python programs and references
- Practice quiz 6: Path integrals
- Bose-Einstein condensation (Quantum Statistical mechanics 3/3)
- Lecture 7: Quantum indiscernability and Bose-Einstein condensation
- Tutorial 7: Permutation cycles and ideal Bosons
- Homework session 7: Bosons in a trap - Bose-Einstein condensation
- Python programs and references
- Practice quiz 7: BEC
- Ising model - Enumerations and Monte Carlo algorithms
- Lecture 8: Ising model - From enumeration to Cluster Monte Carlo Simulations
- Tutorial 8: Ising model - Heat bath algorithm, coupling of Markov chains
- Homework session 8: Cluster sampling, perfect sampling in the Ising model
- Python programs and references
- Practice quiz 8: Spins and Ising model
- Dynamic Monte Carlo, simulated annealing
- Lecture 9: Dynamical Monte Carlo and the Faster-than-the-Clock approach
- Tutorial 9: Simulated Annealing and the 13-sphere problem
- Homework session 9: Simulated Annealing for sphere packings and the travelling salesman problem
- Python programs and references
- The Alpha and the Omega of Monte Carlo, Review, Party
- Lecture 10: The Alpha and the Omega of Monte Carlo
- Tutorial 10: Review - Party - Best of
- Python programs and references
- Final Exam 2016
Summary of User Reviews
Read reviews for the Statistical Mechanics course on Coursera. Users have rated this course highly for its comprehensive coverage of the subject matter. One key aspect that many users appreciated was the clear explanations provided by the instructor.Pros from User Reviews
- Comprehensive coverage of the subject matter
- Engaging and knowledgeable instructor
- Well-structured course materials
- Challenging assignments that help reinforce learning
- Useful supplementary resources
Cons from User Reviews
- Some users found the pace of the course to be too slow
- Course may be difficult to understand for those without a strong math background
- Some users felt that the course could benefit from more interactive elements
- Course may not be suitable for beginners to the subject
- Some users experienced technical difficulties with the online platform
Werner Krauth
4.8 Raiting
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Statistical Mechanics: Algorithms and Computations
- 4.8
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![]()
![]()
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Join in if you are curious (but not necessarily knowledgeable) about algorithms, and about the deep insights into science that you can obtain by the algorithmic approach....
