Difference between revisions of "NUMPHYsandML"

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(Schedule)
(Schedule)
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* [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions (Zoom link [https://zoom.us/j/97382020537?pwd=MXNTdWoxLy9Od0lONDhnU2tXVlJXdz09])
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* [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions  
  
* [https://colab.research.google.com/drive/1nU4E_pFWjSFPNigzy8-LcJpef26IKY6x?usp=sharing Tutorial 4]: Ising model and phase transitions [https://colab.research.google.com/drive/1AEUX3U7hxDR_YYy8ld3knTEVR1KYa9SP?usp=sharing problems] (Zoom link [https://zoom.us/j/97033444403?pwd=NzJPQ3Yxa2JrQW56aVlGOGV0Mk40dz09])
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* [https://colab.research.google.com/drive/1nU4E_pFWjSFPNigzy8-LcJpef26IKY6x?usp=sharing Tutorial 4]: Ising model and phase transitions [https://colab.research.google.com/drive/1AEUX3U7hxDR_YYy8ld3knTEVR1KYa9SP?usp=sharing problems]  
  
  
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'''GoToMeeting link''' [https://global.gotomeeting.com/join/854835733] (Room 1 M2 ICFP)
 
'''GoToMeeting link''' [https://global.gotomeeting.com/join/854835733] (Room 1 M2 ICFP)
  
 +
''' Due: Homework 1'''
  
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[https://drive.google.com/file/d/1JY7PlB00hGpw1814lUyVor37E-um3xQj/view?usp=sharing Lecture 5]: Quantum particle
  
[https://drive.google.com/file/d/1JY7PlB00hGpw1814lUyVor37E-um3xQj/view?usp=sharing Lecture 3]: Quantum particle
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Tutorial 5: Time evolution (quantum)
 
 
Tutorial 3: Time evolution (quantum)
 
 
 
''' Due: Homework 1'''
 
 
 
  
 
Homework 2:
 
Homework 2:
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'''Friday, October 9, 2020'''
 
'''Friday, October 9, 2020'''
  
Lecture 4: Importance sampling
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Lecture 6: Importance sampling
  
Tutorial 4: Faster than the clock algorithms
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Tutorial 6: Faster than the clock algorithms
  
  
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''' Friday, October 16, 2020'''
 
''' Friday, October 16, 2020'''
  
Lecture 5: Optimization & Dijkstra algorithm
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Lecture 7: Optimization & Dijkstra algorithm
  
Tutorial 5: Simulated annealing
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Tutorial 7: Simulated annealing
  
 
''' Due: Homework 2'''
 
''' Due: Homework 2'''
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'''Friday, October 23, 2020'''
 
'''Friday, October 23, 2020'''
  
'''QCM: 2 hours, 20 questions for 20 points'''
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?????????????????????????
 
 
Lecture 5: more on ptimization
 
  
 
== References ==
 
== References ==
 
* [http://www.lps.ens.fr/~krauth/index.php/SMAC SMAC W. Krauth Statistical Mechanics: Algorithms and Computations (Oxford: Oxford University Press) (2006)]
 
* [http://www.lps.ens.fr/~krauth/index.php/SMAC SMAC W. Krauth Statistical Mechanics: Algorithms and Computations (Oxford: Oxford University Press) (2006)]
 
* Other references are specified in each lectures
 
* Other references are specified in each lectures

Revision as of 11:21, 27 September 2020

Numerical Physics and Machine Learning

IMPORTANT

Starting from Friday (25-09-2020) we are changing lecture&tutorial's room. Our new room is L367 (third floor)

The advantage of the new location is that L367 is better equipped (the camera, the video-projector...). The disadvantage is that L367 can only accommodate 24 students and we are slightly overbooking your presence, but from the experience of these weeks we think it is OK.

If you want to come on Friday, we ask you to write your name in the list below:

List of participants

If you do not find a place, please let us know. Thank you very much for your cooperation!


Course description

We will cover many algothims used in many-body problems and complex systems: Monte Carlo methods, molecular dynamics and optmization in complex landscapes. We shall also discuss the use of some machine learning algorithms (Boltzmann machines, Auto-encoder, Deep Learning) for physics problems. We focus on algorithms and physics, not on programming and heavy numerics. The theoretical lecture is followed by a tutorial introducing concrete numerical exercises. You will have to hand in 3 homeworks.


The Team

Where and When

  • Lectures on Fridays: 14:00-16:00
  • Tutorials on Fridays: 16:00-18:00
  • ENS, 24 rue Lhomond, room L367 (third floor)


Computer Requirements

No previous experience in programming is required.

Programming Language: Python

For practical installation, we recommand either to use Anaconda (See Memento Python) or use google colab.
The Colaboratory platform from Google is quite good way to use powerful computer without buying one: It requires no specific hardware or software, and even allows you to use GPU computing for free, all by writting a jupyter notebook that you can then share.

Grading

Homeworks (10 points each) + 1 MCQ (20 points)

Schedule

Friday, September 4, 2020


Friday, September 11, 2020



Friday, September 18, 2020

Homework: Download


Friday, September 25, 2020


  • Lecture 4: Ising model and phase transitions


Friday, October 2, 2020

GoToMeeting link [1] (Room 1 M2 ICFP)

Due: Homework 1

Lecture 5: Quantum particle

Tutorial 5: Time evolution (quantum)

Homework 2:


Friday, October 9, 2020

Lecture 6: Importance sampling

Tutorial 6: Faster than the clock algorithms


Friday, October 16, 2020

Lecture 7: Optimization & Dijkstra algorithm

Tutorial 7: Simulated annealing

Due: Homework 2


Friday, October 23, 2020

?????????????????????????

References