M2 - Digital Physics (PhysNum)

Teaching of mathematics for numerical physics. Introduction of tools for the study of 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 starts with the study of situations where the solution is analytical, then situations where the solution is semi-analytical before attacking on the finite difference method FD. Different FD schemes are proposed with, each time, an evaluation of the convergence according to different key parameters (truncation of the domain, number of samples...). Finally, examples of concrete physical applications are studied.    

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

It addresses the underlying theoretical notions, 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 is an introduction without pre-requisites to scientific image processing, in the context of physics but also of medical sciences.

Starting from the basic elements of digital image coding, we will introduce the main techniques aiming first at improving the quality of image data, and then at extracting quantitative data. Deconvolutions, denoising, thresholding, segmentations, Fourier transforms, wavelets will be presented.

We will conclude with the specific problems posed by image sequences (movies) 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 teaching unit is an introduction to artificial intelligence for physicists. It aims at discovering uses of deep learning using the TensorFlow and Keras libraries. It includes a presentation of examples of use for physics.

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TD courses in English, for students in the Master 2 Physics program, aiming at professional insertion in English in a contemporary context.

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This module is an opportunity for students to discover the specificities of the working world and to prepare themselves to enter it under the best possible conditions, notably through sharing experiences with professional speakers. Students practice applying for a job in a methodical way, optimizing the analysis of the offer, the targeted writing of the CV and the cover letter, the preparation of the job interview (role plays, simulations).

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This unit deals with the solution of electromagnetic problems on a computer. From 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 the problems of diffraction in the harmonic regime by a bounded obstacle will be given for the case of scalar waves in 2D and 3D.

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

It thus includes the extension of the methods already acquired, both in terms of physics (ab initio simulations, density functional theory) as well as in terms of implementation (optimization, parallelization) and implementation (initiation 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 the development of a software for research and development and/or teaching.

This project completes the experience acquired during the tutored project already done in M1. This time the students work individually which is a different experience from the M1 project done in group. The student receives an order to create a software that meets a set of specifications and must deliver a functional code.   

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Six-month M2 internship (25 ECTS) carried out in a company or a public organization (research laboratory, national agency, etc.).

The internship must focus on a physical problem in which a numerical calculation component is involved.  

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