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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.

The Team

Where and When

  • Lectures on Fridays: 14:00-16:00
  • Tutorials on Fridays: 16:00-18:00
  • ENS, 24 rue Lhomond, room Conf IV (2nd 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.


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


Friday, September 4, 2020

Friday, September 11, 2020

Zoom link [1]

Lecture 2: Basic Sampling

Tutorial 2: Markov matrix

Friday, September 18, 2020

Lecture 2: Error evaluation

Tutorial 2: Thumb rule

Friday, September 25, 2020

Lecture 3: Ising model and phase transitions

Tutorial 3: Ising model and phase transitions

Due: Homework 1

Friday, October 2, 2020

Lecture 3: Quantum particle

Tutorial 3: Time evolution (quantum)

Homework 2:

Friday, October 9, 2020

Lecture 4: Importance sampling

Tutorial 4: Faster than the clock algorithms

Friday, October 16, 2020

Lecture 5: Optimization & Dijkstra algorithm

Tutorial 5: Simulated annealing

Due: Homework 2

Friday, October 23, 2020

QCM: 2 hours, 20 questions for 20 points

Lecture 5: more on ptimization