Statistical Mechanics: Algorithms and Computations
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- Public/Government Institute
Statistical Mechanics: Algorithms and Computations at Coursera Overview
Duration | 16 hours |
Total fee | Free |
Mode of learning | Online |
Schedule type | Self paced |
Official Website | Explore Free Course  |
Credential | Certificate |
- Overview
- Highlights
- Course Details
- Curriculum
Statistical Mechanics: Algorithms and Computations at Coursera Highlights
- 100% online Start instantly and learn at your own schedule.
- Flexible deadlines Reset deadlines in accordance to your schedule.
- Approx. 16 hours to complete
- English Subtitles: French, Portuguese (European), Russian, English, Spanish
Statistical Mechanics: Algorithms and Computations at Coursera Course details
- 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.
Statistical Mechanics: Algorithms and Computations at Coursera Curriculum
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
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