Statistical Mechanics: Algorithms and Computations
Master computational techniques for statistical mechanics, from Monte Carlo methods to quantum systems.
Master computational techniques for statistical mechanics, from Monte Carlo methods to quantum systems.
Dive into the fascinating world of statistical mechanics through a computational lens. This course bridges classical and quantum physics using algorithmic approaches. Learn Monte Carlo techniques, explore hard-disk systems, and understand entropic interactions. Delve into quantum statistical mechanics, including density matrices, path integrals, and Bose-Einstein condensation. Study the Ising model and dynamic Monte Carlo methods. Gain hands-on experience with Python programming to simulate complex physical systems. Ideal for students and researchers in physics, chemistry, and computer science seeking to apply computational methods to statistical mechanics problems.
4.8
(264 ratings)
37,301 already enrolled
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English
پښتو, বাংলা, اردو, 2 more
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What you'll learn
Implement and analyze Monte Carlo algorithms for various physical systems
Understand and apply classical and quantum statistical mechanics principles computationally
Develop simulations for hard-disk systems and entropic interactions
Apply path integral techniques to quantum systems
Simulate Bose-Einstein condensation using computational methods
Implement and analyze Ising model simulations using various algorithms
Skills you'll gain
This course includes:
8.42 Hours PreRecorded video
9 assignments
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FullTime access
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There are 10 modules in this course
This course provides a comprehensive introduction to computational methods in statistical mechanics, bridging classical and quantum physics. Students will learn various Monte Carlo techniques, including direct sampling and Markov-chain methods, and apply them to systems such as hard disks and spin models. The curriculum covers both classical statistical mechanics topics like entropic interactions and phase transitions, as well as quantum concepts including density matrices, path integrals, and Bose-Einstein condensation. Learners will explore advanced sampling techniques, cluster algorithms, and optimization methods like simulated annealing. Throughout the course, students will implement algorithms using Python, gaining practical experience in computational physics. The course emphasizes the connection between physical principles and computational techniques, providing a solid foundation for research in modern statistical mechanics.
Monte Carlo algorithms (Direct sampling, Markov-chain sampling)
Module 1 · 1 Hours to complete
Hard disks: From Classical Mechanics to Statistical Mechanics
Module 2 · 1 Hours to complete
Entropic interactions and phase transitions
Module 3 · 1 Hours to complete
Sampling and integration
Module 4 · 1 Hours to complete
Density matrices and Path integrals (Quantum Statistical mechanics 1/3)
Module 5 · 1 Hours to complete
Lévy Quantum Paths (Quantum Statistical mechanics 2/3)
Module 6 · 1 Hours to complete
Bose-Einstein condensation (Quantum Statistical mechanics 3/3)
Module 7 · 1 Hours to complete
Ising model - Enumerations and Monte Carlo algorithms
Module 8 · 1 Hours to complete
Dynamic Monte Carlo, simulated annealing
Module 9 · 57 Minutes to complete
The Alpha and the Omega of Monte Carlo, Review, Party
Module 10 · 1 Hours to complete
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Instructor
Research Director at the CNRS
I am a theoretical physicist, CNRS research director, Director of the Physics Department at Ecole normale supérieure. Visit website for details on vita, research and teaching.
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4.8 course rating
264 ratings
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