CoDaDri

Computational and Data Driven Physics

Modern physics is characterized by an increasing complexity of systems under investigation, in domains as diverse as condensed matter, astrophysics, biophysics, etc. Establishing adequate models to describe these systems and being able to make quantitative predictions from those models is extremely challenging.

Course description

We will first cover many algorithms used in many-body problems and complex systems, with special emphasis on Monte Carlo methods, molecular dynamics, and optimization in complex landscapes.

Second, we will provide statistical inference and machine learning tools to harness the growing availability of experimental data to design accurate models of the underlying, complex, strongly non-homogeneous and interacting systems.

Each theoretical lecture will be followed by a tutorial illustrating the concepts with practical applications borrowed from various domains of physics. We will focus on methods and algorithms and physics, not on programming and heavy numerics! You will have to hand in 3 homeworks.

The Team

• Alberto Rosso (Computationa Physics)
• Remy Monasson (Data Driven Physics)
• Simona Cocco & [ https://www.cpht.polytechnique.fr/?q=en/node/110 Michel Ferrero] (Tutorials)

Where and When

• Lectures on Fridays: 14:00-16:00
• Tutorials on Fridays: 16:00-18:00
• ENS, 29 rue D'Ulm, salle Borel + Djebar

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 (50% of the mark) + 1 MCQ (50% of the final mark)

Schedule

Friday, September 3, 2020

• Lecture 1 Introduction to Monte Carlo

• Tutorial 1 Markov Matrix

Friday, September 10, 2020

• Tutorial 2 Markov matrix problems

• Tutorial 3 Thumb rule problems

\\ Homework: Download

Friday, September 17, 2020

• Lecture 2 Basic Sampling

• Lecture 3: Errors and Precision

Friday, September 24, 2020

• Lecture 4: Ising model and phase transitions

• Tutorial 4: Ising model and phase transitions problems

Friday, October 1, 2020

Lecture 6: Importance sampling

Tutorial 6: Faster than the clock algorithms problems

Homework 2: Download

Friday, October 5, 2020

Lecture 7: Optimization & Dijkstra algorithm

Tutorial 7: Simulated annealing problems