Difference between revisions of "NUMPHYsandML"

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(Oral exams December 9-11 AND FRIDAY 13)
(Course description)
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NumPhys is a general course in Computational Physics, with applications in Statistical Physics and Condensed Matter.
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We cover the many algorithms used in modern many-body problems: molecular dynamics, Monte Carlo (classical and quantum), exact diagonalization and optmization in complex landscapes. Implications to computer science are also discussed. We focus on algorithms and physics, not on programming and heavy numerics. The theoretical lecture is followed by a tutorial introducing many concrete numerical exercises. You will have to hand in 3 homeworks.
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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.
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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.
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Revision as of 12:54, 31 August 2020

Sinaï's billard

Numerical Physics and Machine Learning

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.

Team

Schedule

  • Lectures on Fridays: 14.0-16.00
  • Tutorials on Fridays: 16h00-18.00
  • ENS, 24 rue Lhomond, room Conf IV (2nd floor)

Here you find the scheduling of Lectures, Tutorials and Homeworks


Language

The working language for this course is English.
Programming Language: Python 3. See Memento Python
No previous experience in programming is required.
You need first of all to have Python installed with at least modules NumPy, SciPy and matplotlib.

Grading

3 homeworks (10 points each) + 1 MCQ (20 points) + 1 oral exam (50 points)

WIFI

The WIFI network is: PHYS-GUEST

The WIFI password for this networks is: PhysiqueENS

Then you can run a navigator (Firefox, Chrome, IE, Safari...)

Accept the certificat and enter the password: numerical.physics@phys.ens.fr


Forum

here it is please register


References