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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. The goal of the course is to provide the tools and concepts necessary to tackle those systems.

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

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


If you have questions or want to discuss topics related to the lecture, to the exercises or to the homeworks, you can use the Computational and Data Driven Physics Slack. In order to join the Slack use the following invitation link.

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 Collaboratory 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.


Computational Physics:

  • Homework 1: 5 points
  • Homework 2: 5 points
  • Multiple Choice Questions in November: 10 points

Data Driven Physics:

  • Final exam in January: 20 points
 == Schedule ==