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.
教學大綱 - 您將從這門課程中學到什麼
週
1完成時間為 1 小時
Monte Carlo algorithms (Direct sampling, Markov-chain sampling)
Dear students,
welcome to the first week of Statistical Mechanics: Algorithms and Computations!
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Here are a few details about the structure of the course: For each week, a lecture and a tutorial videos will be presented, together with a downloadable copy of all the relevant python programs mentioned in the videos. Some in-video questions and practice quizzes will help you to review the material, with no effect on the final grade. A mandatory peer-graded assignment is also present, for weeks from 1 to 9, and it will expand on the lectures' topics, letting you reach a deeper understanding. The nine peer-graded assignments will make up for 50% of the grade, while the other half will come from a final exam, after the last lecture.
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In this first week, we will learn about algorithms by playing with a pebble on the Monte Carlo beach and at the Monaco heliport. In the tutorial we will use the 3x3 pebble game to understand the essential concepts of Monte Carlo techniques (detailed balance, irreducibility, and a-periodicity), and meet the celebrated Metropolis algorithm. Finally, the homework session will let you understand some useful aspects of Markov-chain Monte Carlo, related to convergence and error estimations.
3 個視頻 （總計 62 分鐘）, 2 個閱讀材料, 2 個測驗
3 個視頻
Tutorial 1: Exponential convergence and the 3x3 pebble game32分鐘
Homework Session 1: From the one-half rule to the bunching method1分鐘
2 個閱讀材料
Python programs and references10分鐘
Errata (Lecture 1)10分鐘
1 個練習
Practice quiz 1: spotting a correct algorithm4分鐘
週
2完成時間為 1 小時
Hard disks: From Classical Mechanics to Statistical Mechanics
In Week 2, you will get in touch with the hard-disk model, which was first simulated by Molecular Dynamics in the 1950's. We will describe the difference between direct sampling and Markov-chain sampling, and also study the connection of Monte Carlo and Molecular Dynamics algorithms, that is, the interface between Newtonian mechanics and statistical mechanics. The tutorial includes classical concepts from statistical physics (partition function, virial expansion, ...), and the homework session will show that the equiprobability principle might be more subtle than expected.
3 個視頻 （總計 71 分鐘）, 1 個閱讀材料, 2 個測驗
3 個視頻
Tutorial 2: Equiprobability, partition functions, and virial expansions for hard disks32分鐘
Homework Session 2: Paradoxes of hard-disk simulations in a box2分鐘
1 個閱讀材料
Python programs and references10分鐘
1 個練習
Practice quiz 2: spotting a correct algorithm (continued)4分鐘
週
3完成時間為 1 小時
Entropic interactions and phase transitions
After the hard disks of Week 2, in Week 3 we switch to clothe-pins aligned on a washing line. This is a great model to learn about the entropic interactions, coming only from statistical-mechanics considerations. In the tutorial you will see an example of a typical situation: Having an exact solution often corresponds to finding a perfect algorithm to sample configurations. Finally, in the homework session we will go back to hard disks, and get a simple evidence of the transition between a liquid and a solid, for a two-dimensional system.
3 個視頻 （總計 62 分鐘）, 2 個閱讀材料, 2 個測驗
3 個視頻
Tutorial 3: Algorithms, exact solutions, thermodynamic limit31分鐘
Homework Session 3: Two-dimensional liquids and solids2分鐘
2 個閱讀材料
Python programs and references10分鐘
Errata (Tutorial 3)10分鐘
1 個練習
Practice quiz 3: Spotting a correct algorithm (continued)4分鐘
週
4完成時間為 1 小時
Sampling and integration
In Week 4 we will deepen our understanding of sampling, and its connection with integration, and this will allow us to introduce another pillar of statistical mechanics (after the equiprobability principle): the Maxwell and Boltzmann distributions of velocities and energies. In the homework session, we will push the limits of sampling until we can compute the integral of a sphere... in 200 dimensions!
3 個視頻 （總計 69 分鐘）, 1 個閱讀材料, 2 個測驗
3 個視頻
Tutorial 4: Sampling discrete and one-dimensional distributions34分鐘
Homework Session 4: Sampling and integration in high dimensions2分鐘
1 個閱讀材料
Python programs and references10分鐘
1 個練習
Practice quiz 4: four disks in a box6分鐘
4.8
62 個審閱25%
通過此課程獲得實實在在的工作福利
33%
加薪或升職
熱門審閱
Excellent and enthusiastic lectures and tutorials covering a number of topics. Much of the learning took place in the assignments where the concepts were applied and various points were illustrated.
I really enjoyed the course. The only problem was that I was using python 3+ and the programs were written with python 2+. There are some minor differences but I figured the them easily.
講師
Werner Krauth
Directeur de recherches au CNRSDepartment of physics
關於 法国巴黎高等师范学院
L’École normale supérieure (ENS) est un établissement d'enseignement supérieur pour les études prédoctorales et doctorales (graduate school) et un haut lieu de la recherche française. L'ENS offre à 300 nouveaux étudiants et 200 doctorants chaque année une formation de haut niveau, largement pluridisciplinaire, des humanités et sciences sociales aux sciences dures. Régulièrement distinguée au niveau international, l'ENS a formé 10 médailles Fields et 13 prix Nobel.
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