Level of study
BAC +4
ECTS
5 credits
Component
Faculty of Science
Hourly volume
42h
Description
This UE includes a refresher and deepening of programming techniques as well as an introduction to numerical physics. We will start with a review of procedural programming with the Python 3 language. Then we will take an in-depth look at numerical methods relevant to physics, studying a selection of classical algorithms from numerical analysis and applying them to physical problems.
Objectives
Learn to program on an advanced level with Python and know how to apply its knowledge in scientific programming. Know the notions of numerical error, numerical stability and algorithmic complexity. Know and know how to implement selected methods for the numerical calculation of integrals, for the solution of ordinary and partial differential equations and for Monte Carlo sampling.
Necessary pre-requisites
Basic programming skills. Knowledge of computer science, physics, and mathematics at the undergraduate level.
Recommended prerequisites
Good practice of Python 3 and its modules, especially NumPy. Bachelor's degree training in programming and numerical physics, in particular either "Programming for Physics" or "Simulation Tools" in L3 or equivalent.
Knowledge control
Continuous control
Syllabus
Procedural programming with Python 3 (revision and deepening)
Scientific programming, the NumPy library
Graphics with Matplotlib
Notions of numerical error, stability and algorithmic complexity
Numerical quadrature methods: Newton-Cotes methods, adaptive methods, Gauss quadrature
Ordinary differential equations: Runge-Kutta methods, implicit methods, adaptive methods
Finite difference methods for partial differential equations
Monte Carlo sampling and integration