Summer School on Numerics for Physics

École d’été 2026 « Outils numériques pour la Physique », organisée dans le cadre de la graduate school Extrem.

Organizers and speakers¶

• Jonathan Ferreira

• Mourad Ismail

• Pierre Augier

• Isabelle Joncour

• Nicolás Cuello

• Damien Caliste

Table of contents¶

• School on numerics, Extrem gradual school
• Preliminary
  • Practical organisation
    • École d’été 2026
    • Programme 2026
    • Partie Généralités 2026
  • Before the school
    • Python CNRS setup
    • Common dev software
    • Clone the repository of the school
    • First steps with a supercomputer with Mesonet
• Computing and coding
  • First part
    • Unix systems
    • Terminals, shells and consoles
    • First steps with Bash and Linux
    • Languages and compilation
    • Files, folders, binaries and libraries
    • Secure Shell - SSH
    • Good practices for coding
    • About software installations
    • First steps on a supercomputer
    • Open-source and open-science
    • How to search and AI
  • Second part
    • Hardware and parallel computing
    • Versioning tools, web development platforms and continuous integration
    • Short introduction to Mercurial
    • Modern packaging for Python
    • Testing and test driven development
    • Debugging
    • How to save data?
    • Optimization and efficiency
      • Profiling: different types and tools
      • Example n-body
• Numerical methods
  • Practicals on few numerical methods
  • Fluids and astrophysics
    • Install Fluidsim with MPI support
    • TP FluidSim – Étude de la turbulence océanique
    • TP Fargo3D – Migration planétaire dans les disques École de M1 (UGA, Grenoble)
  • Quantum mechanics and Density Functional Theory
    • Nitrogen-rich atmospheres, DFT study of a precursor molecule to life’s building block molecules: HCN
    • Investigating anode charge and discharge in Li-ion batteries by Density Functional Theory calculations
  • Machine learning
    • Galaxy Zoo collection and deep learning classification
    • References for Machine and Deep Learning
    • MACHINE LEARNING ON TABULAR DATA - Part 1 - Classification of stars
    • MACHINE LEARNING ON IMAGE DATA - Part 2 - DEEP LEARNING applied on Classification of galaxies based on their morphology
  • Monte-Carlo Methods
    • Introduction
    • Event generation with Pythia
    • Classification with TMVA/ROOT
• Other
  • References
  • How to contribute
  • Archives
    • École d’été 2025
    • Programme 2025