Computational Physics (PhysNum)

Fluids are all around us at all times and on every scale. Understanding fluid mechanics means understanding the mechanics of our surroundings—air and water, in particular. As such, hydrodynamics is part of a physicist’s basic knowledge.

The course “Hydrodynamics” provides an introduction to the mechanics of ideal incompressible fluids (Euler) and Newtonian viscous fluids (Navier-Stokes). Classical flow patterns are presented, along with the concepts of the boundary layer, instability, and turbulence. The focus is on physical concepts rather than on advanced mathematical or numerical solution methods.

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English tutorial course for students in the Master's 1 Physics program who are working toward professional proficiency in scientific English.

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This course is one of the cornerstones of modern physics. It provides a body of knowledge that is strictly necessary for all physics tracks, as it lays the groundwork for the theoretical description of the interaction between the electromagnetic field and elementary quantum systems such as two-level systems, atoms, and molecules. It also provides the knowledge necessary for understanding lasers, modern optical devices, and spectroscopic methods and analyses.

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Through two specific examples (X-ray diffraction and vibrations), this module shows in detail how the physical properties of a solid are modeled. The formalism will also be applied to finite systems, such as nanoparticles, and will remain valid for amorphous materials; however, special attention will be given to periodic systems (ranging from linear chains to protein crystals, including graphene and silicon). The concept of reciprocal lattice will naturally arise in connection with this periodicity.

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Introduction to Advanced Statistical Physics: grand canonical ensemble; quantum statistics; quantum fluids (Bose-Einstein condensation, thermal radiation; Sommerfeld theory); phase transitions; Ising model; mean-field theory; dynamics of complex systems.

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A 10-ECTS supervised project during which groups of students work on developing software for research or teaching.

This project is designed to give students their first semi-professional experience by working in groups of two or more on a fairly large-scale project, typically proposed by fellow researchers who wish to develop and/or expand software intended for research purposes or for the general public.

Supervision is provided by fellow physicists and, in some cases, computer scientists. Students submit their code along with documentation. A report is written, and an oral defense is held.    

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The EU course “Condensed Matter Physics 2: Electronic Properties” is intended for students interested in solid-state physics.

Following on from the course unit “Condensed Matter Physics 1: Structural Properties,” this course unit covers the properties of electrons in crystalline solids, the band structure of electronic energy levels, and the basic concepts of semiconductor physics.

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Modern experimental physics generally requires the setup of a data acquisition system of varying complexity involving different types of instruments—such as sources, sensors, actuators, etc.—and a controller (such as a computer). The objective of this course unit is to familiarize students with this type of problem so that they can set up such a data acquisition system. On the controller side, the control logic will be implemented in Python (specifically using the PyVisa library).  

- Overview of the most common communication interfaces/ports: serial (RS-232, USB), parallel (GPIB), and network (Ethernet) (CM).

- Implementation of simple examples of communication, device configuration, and data acquisition (lab).

- Development of a more comprehensive data acquisition system through projects (practical training). 

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Knowing how to collect and process data are essential skills in a scientific and/or technical professional setting. The objective of this course is to address, as effectively as possible, three types of standard professional skills:

· Advanced use of spreadsheets and graphing software (MS Excel, LO-CALC) for scientific and technical purposes

· Network interconnections: infrastructure, the TCP/IP protocol suite, security

· Introduction to relational databases (MS Access, LO-BASE) – concepts and terminology, creating queries, graphical reports, forms.

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Mathematics for Computational Physics. Introduction to 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 provide students with skills in the numerical solution of the Schrödinger equation in order to simulate complex quantum well structures. The course begins with a study of cases where the solution is analytical, followed by cases where the solution is semi-analytical, before moving on to the finite difference (FD) method. Various FD schemes are presented, each accompanied by an evaluation of convergence as a function of key parameters (domain truncation, number of samples, etc.). Finally, examples of concrete physical applications are examined.    

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This course lays the groundwork for using “atomistic” simulation tools—that is, those based on microscopic interactions between constituents. Primarily, it lays the foundation for simulations known as “Molecular Dynamics” and “Monte Carlo.”

It covers the underlying theoretical concepts to build a solid understanding of the methods, as well as the practical implementation of the corresponding code.

The critical and reasoned analysis of data is also discussed.

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This course is an introductory course with no prerequisites on scientific image processing, in the context of both physics and the medical sciences.

Starting with the fundamentals of digital image coding, we will introduce the main techniques aimed first at improving image quality and then at extracting quantitative data from images. The course will cover deconvolution, noise reduction, thresholding, segmentation, Fourier transforms, and wavelets.

We will conclude by discussing the specific challenges 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 explore applications of deep learning using the TensorFlow and Keras libraries. It includes a presentation of examples of how deep learning is used in physics.

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English tutorial course for students in the Master 2 Physics program who are preparing for a career in an English-speaking professional environment in a contemporary context.

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This module gives students the opportunity to learn about the unique aspects of the professional world and to prepare to enter it under the best possible conditions, particularly through sharing experiences with professionals in the field. Students practice successfully completing the job application process in a systematic manner by thoroughly analyzing job postings, crafting targeted resumes and cover letters, and preparing for job interviews (through role-playing and simulations).

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This course unit covers the computer-based solution of electromagnetism problems. Using Maxwell’s equations, it demonstrates how to simulate the behavior of electromagnetic waves in various 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 the harmonic regime caused by a bounded obstacle will be provided for the case of scalar waves in 2D and 3D.

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This module provides an introduction to advanced applications of atomistic simulation methods, particularly molecular dynamics.

It thus involves expanding on the methods already learned, both in the field of physics (ab initio simulations, density functional theory) and in terms of implementation (optimization, parallelization) and practical application (an introduction to conducting simulations in a high-performance computing environment).

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A 5-ECTS supervised project during which students work individually to develop software for research and development and/or teaching.

This project builds on the experience gained during the supervised project completed in the first year of the master’s program. This time, students work individually, which offers a different experience from the group-based project in the first year of the master’s program. Students are assigned the task of developing software that meets specific requirements and must deliver functional code.   

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A six-month M2 internship (25 ECTS) completed at a company or public organization (research laboratory, national agency, etc.).

The internship must focus on a physics problem that involves a numerical computation component.  

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