M2 - Computational Physics (PhysNum)

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

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

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This course is designed to provide students with skills in the field of numerical solution of the Schrödinger equation in order to simulate complex quantum well structures. The course begins by studying situations where the solution is analytical, then situations where the solution is semi-analytical, before moving on to the finite difference (FD) method. Various FD schemes are proposed, each with an evaluation of convergence based on different 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. Primarily, it lays the foundations for simulations known as "Molecular Dynamics" and "Monte Carlo."

He addresses the underlying theoretical concepts in order to build a solid 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 an introduction to scientific image processing, with no prerequisites, in the context of physics and medical sciences.

Starting with the basics of digital image coding, we will introduce the main techniques aimed first at improving image data quality, then at extracting quantitative data. Deconvolution, denoising, thresholding, segmentation, Fourier transforms, and wavelets will be covered.

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

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English tutorial course for students enrolled in the Master 2 Physics program who are seeking professional integration in English in a contemporary context.

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This module gives students the opportunity to discover the specifics of the world of work and prepare themselves to enter it in the best possible conditions, in particular through sharing experiences with professionals from the field. Students practice how to successfully apply for a job, using a methodical approach, optimizing their analysis of the job offer, writing a targeted resume and cover letter, and preparing for the job interview (role-playing, simulations).

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This teaching unit deals with solving electromagnetic problems using computers. Based on Maxwell's equations, it shows how to simulate the behavior of electromagnetic waves in different media. In particular, it includes a detailed implementation of simulations based on the finite difference time domain (FDTD) method.

An introduction to diffraction problems in 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 advanced practices in atomistic simulation methods, particularly Molecular Dynamics.

It thus includes the expansion of methods already acquired, both in terms of physics (ab initio simulations, density functional theory) , as well as 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 supervised project during which students work individually on developing software for research and development and/or teaching.

This project builds on the experience gained during the supervised project already completed in M1. This time, students work individually, which is a different experience from the M1 project, which was carried out in groups. Students receive an order to develop software that meets specific specifications and must deliver functional code.   

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

The internship must focus on a physical problem involving a numerical calculation component.  

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