M2 - Digital Physics (PhysNum)

Teaching mathematics for numerical physics. Introduction of tools for studying partial differential equations (distributions, variational formulation, Sobolev spaces).

Introduction to integral methods and their numerical implementation. Applications to diffraction problems in the harmonic regime.

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This course is designed to give students skills in the numerical solution of the Schrödinger equation in order to simulate complex quantum well structures. The course begins with the study of situations where the solution is analytical, followed by situations where the solution is semi-analytical, before tackling the finite-difference method DF. Different DF schemes are proposed, each time with an evaluation of convergence as a function of various key parameters (domain truncation, number of samples, etc.). Finally, examples of concrete physical applications are studied.    

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This course lays the foundations for using 'atomistic' simulation tools, i.e. those based on microscopic interactions between constituents. In particular, it lays the foundations for 'Molecular Dynamics' and 'Monte Carlo' simulations.

It covers the underlying theoretical concepts, in order to build a good understanding of the methods, as well as the practical implementation of the corresponding codes.

The critical and reasoned use of data is also discussed.

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This course provides a no-obligation introduction to scientific image processing, in the context of both physics and the medical sciences.

Starting with the basics of digital image coding, we will introduce the main techniques for improving the quality of image data, and then extracting quantitative data. Deconvolutions, denoising, then thresholding, segmentations, Fourier transforms and wavelets will be on the program.

We will conclude with the specific problems posed by image sequences (films) or 3D images such as MRI data in a medical context.

The tool used will be the Matlab/Octave programming environment.

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This course is an introduction to artificial intelligence for physicists. It aims to discover the uses of deep learning using the TensorFlow and Keras libraries. It includes a presentation of examples of use in physics.

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TD courses in English, for students in the Master 2 Physics program, who want to work in English in a contemporary context.

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This module is an opportunity for students to discover the specifics of the world of work and prepare to enter it under the best possible conditions, notably through experience-sharing with speakers from the professional world. Students learn how to put together a successful job application, optimizing the analysis of the job offer, writing a targeted CV and cover letter, and preparing for the job interview (role-playing, simulations).

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This teaching unit deals with solving electromagnetic problems on the computer. Based on Maxwell's equations, it shows how to simulate the behavior of electromagnetic waves in different media. It includes a detailed implementation of simulations based on the Finite Difference Time Domain (FDTD) method.

An introduction to diffraction problems in the harmonic regime by a bounded obstacle will be given for the case of 2D and 3D scalar waves.

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This module introduces advanced atomistic simulation methods, and Molecular Dynamics in particular.

It thus includes an extension of the methods already acquired, both in terms of physics (ab initio simulations, density functional theory) and in terms of implementation (optimization, parallelization) and application (introduction to the practice of simulations in a high-performance computing environment).

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A 5 ECTS tutored project during which students work individually on developing software for research and development and/or teaching.

This project completes the experience acquired during the tutored project already carried out in M1. This time, students work individually, which is a different experience from the M1 group project. The student is commissioned to produce a software program to a set of well-defined specifications, and is required to deliver functional code.   

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Six-month M2 internship (25 ECTS) with a company or public body (research laboratory, national agency, etc.).

The internship must focus on a physics problem with a numerical computing component.  

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