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返回到 统计力学:算法和计算

学生对 法国巴黎高等师范学院 提供的 统计力学:算法和计算 的评价和反馈

4.8
178 个评分
62 个审阅

课程概述

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....

热门审阅

KL

Sep 23, 2017

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.

MH

Mar 08, 2018

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.

筛选依据:

1 - 统计力学:算法和计算 的 25 个评论(共 60 个)

创建者 Ahmad M

Jan 31, 2019

i love this course, but most codes were not properly explained and rather difficult to tweak.

创建者 jadoul m

Nov 02, 2018

One of the three best MOOCs I have seen.

创建者 Kunal L

Sep 23, 2017

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.

创建者 Marcus S

Mar 27, 2017

Nice but demanding course. No course certficate is available after passing the course...

创建者 Tomas B

Feb 08, 2019

Good course. Nice focus on methods over theory.

创建者 TATENDA Y G

Dec 27, 2018

This is one of the most underrated courses I tell you.

Learn this and you will be an expendable Data Scientist

创建者 GIRISH B

Oct 19, 2018

Great job!!! :D

创建者 Le Q L

Aug 29, 2018

A good course in introducing statistical mechanics concept with programming! If there are other advanced courses, I'm willing to take!

创建者 Wan-Yi W

Aug 25, 2018

Very helpful to me.

创建者 Kwan-Yuet H

Aug 23, 2018

Highly recommended. Old subjects with new technology!

创建者 Thomas B

Jun 04, 2018

It's very interesting to follow. However most of the lectures are very slow and reiterative except when more info is needed (e.g. detailed math).

创建者 José J B d M

Apr 08, 2018

It was a very interesting course, both for having a look on Statistical Mechanic

创建者 霍永学

Mar 25, 2018

So awesome! This is a marvelous course!

创建者 Efren S

Mar 09, 2018

Most useful course I've had a quite some time. Thank you Monsieur Krauth!

创建者 Mehdi H

Mar 08, 2018

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.

创建者 Michele G

Mar 05, 2018

This is a really great course! The concepts proposed here are kind of advanced for non physicists (and a full understanding of all the theory beyod it would require much more than 2 and a half months!), but the course is so well managed and the lecturers are so good that I think that most graduate people from semi-thechnical fields can keep up and be very satisfied about everything!

创建者 César A L

Dec 22, 2017

You will learn not only the theory about how to solve differente many body problems, but you will laso will adquire the hability to ptogram the solutions for any incoming value in almost any related problem

the best statiscal mechanics course i've taken in my whole live. I also bought the book by Werner, it's very well written

创建者 Beakal A

Dec 06, 2017

I very interesting course, the course materials are challenging and is by no means an easy course. But in the end it is very rewarding when you understand them. You will understand statistical mechanics from a intriguing point of view. A must take course, if you want to take your physics and computations to another level.

创建者 Tim B

Oct 24, 2017

Some of the lessons are very difficult, but if you persevere, it's very rewarding.

创建者 Marc Z

Oct 13, 2017

Very didactic!

创建者 wali_1314

Oct 10, 2017

brilliant!!!

创建者 Xu H

Sep 15, 2017

It helps deepen my understanding about Mont Carlo. I had a lot of fun in programing and reading codes or opinions from other students. Our lovely teachers are humorous. They even prepared a big Party at the end of this course XD. hf gl

创建者 Jiting T (

Sep 09, 2017

This is a graduate or advanced undergraduate level class on statistical physics, focusing on the computational tools (MC and MD). The materials are organized very well and the concepts are illustrated in a clear way. A lot of Python examples are provided to help students master the contents. The homework and exam is not hard, as most of the code is already present by the teachers, and students only need to fill the blank or do a little changes. It's not difficult to go through this course and pass the exam, but it's truly difficult to deeply understand all the materials. Although, for the guys who love statistical mechanics, this course deserve your effects.

创建者 RLee

Aug 28, 2017

Engage students with the world of Statistical Mechanics by making hands dirty. One needs to have some basics in Quantum Mechanics or Thermodynamics in order to make sense of what have been done. Not sufficient mathematical proof and intuition could be found in Professor's textbook, although it is good to have it free. The solutions to Newton's packing problem is a kind of surprise. Not sufficient conclusions to problems like with and without boundaries; one-half rules; violation of tabula rasa rules; rejection-free direct sampling to avoid Metropolis Algorithm; simulated annealing. These gaps need to be filled in order to make it more self-sufficient. But still it is a very sincere effort to promote this branch of Physics to the world. It is very transferable to Mathematical Finance and Artificial Intelligence.

创建者 Erik P

Aug 20, 2017

Very clear and very interesting! The exercises are a bit difficult (especially for me that I'm only a beginner in Python) but it's a powerful introduction to computational condensed matter physics!

I suggest it for people that already has the rudiments of Mechanics, Statistical Mechanics and algorithmic approach to every-day problems