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Lecture

Quantum Mechanics: Schrödinger Equation

In course

PHYS-203: Computational physics I

Aborder, formuler et résoudre des problèmes de physique en utilisant des méthodes numériques simples. Comprendre les avantages et les limites de ces méthodes (stabilité, convergence). Illustrer différents sujets de physique traités dans d'autres cours. ...

Description

This lecture covers the application of the Schrödinger equation in quantum mechanics, focusing on numerical methods and approximations. It discusses the WKB approximation, separation of scales, and the discretization of spatial and temporal operators. The presentation includes the development of numerical schemes, such as semi-implicit methods and the Crank-Nicolson scheme, ensuring conservation of probability. The lecture also explores the properties of observables in quantum mechanics, emphasizing Hermitian operators and the probabilistic interpretation of mean values and standard deviations.

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Instructor

Laurent Villard

Originaire de Vauffelin (BE), Laurent Villard est né en 1961. Après avoir obtenu le diplôme d'ingénieur physicien de l'EPFL en 1983, il est assistant au CRPP où il entame une thèse. Ses études portent sur l'étude théorique du chauffage des plasmas par ondes radiofréquences, développant pour cela un code de calcul numérique sous contrat avec le Joint European Torus (JET, Angleterre). Il obtient en 1987 le doctorat ès sciences. En 1988, il est engagé en tant que chargé de cours à l'Université d'Addis Abeba, enseignant l'électromagnétisme et l'électrodynamique. En 1989 et 1990, il rejoint le Polytechnic Education Development Centre de l'Institute of Technology of Bandung (ITB, Indonésie). Ses activités contribuent à la formation et au soutien du personnel enseignant des dix-sept écoles polytechniques nouvellement créées en Indonésie. A la fin 1990, il revient au CRPP en tant que collaborateur scientifique. En 1993 il est nommé professeur assistant. En 1999, il est nommé maître d'enseignement et de recherche, puis, en 2005, professeur titulaire. En collaboration avec JET et General Atomics (USA) il étudie les instabilités qui peuvent être provoquées par les particules énergétiques issues du processus de fusion nucléaire. Avec des scientifiques du Keldysh Institute (Moscou), il s'intéresse au calcul de l'équilibre et de la stabilité de configurations du type tokamak. Enfin, en collaboration notamment avec le Max-Planck Institute fuer Plasma Physik, ses recherches portent sur la simulation numérique, par calcul à haute performance (HPC), de la turbulence dans les plasmas magnétisés.

Official source

Ontological neighbourhood

Mathematics

Analysis: Numerical analysis

Statistics

Statistical inference: Mathematical statistics

Related concepts (32)

Numerical analysis

Numerical analysis is the study of algorithms that use numerical approximation (as opposed to symbolic manipulations) for the problems of mathematical analysis (as distinguished from discrete mathematics). It is the study of numerical methods that attempt at finding approximate solutions of problems rather than the exact ones. Numerical analysis finds application in all fields of engineering and the physical sciences, and in the 21st century also the life and social sciences, medicine, business and even the arts.

Numerical stability

In the mathematical subfield of numerical analysis, numerical stability is a generally desirable property of numerical algorithms. The precise definition of stability depends on the context. One is numerical linear algebra and the other is algorithms for solving ordinary and partial differential equations by discrete approximation. In numerical linear algebra, the principal concern is instabilities caused by proximity to singularities of various kinds, such as very small or nearly colliding eigenvalues.

Numerical methods for ordinary differential equations

Numerical methods for ordinary differential equations are methods used to find numerical approximations to the solutions of ordinary differential equations (ODEs). Their use is also known as "numerical integration", although this term can also refer to the computation of integrals. Many differential equations cannot be solved exactly. For practical purposes, however – such as in engineering – a numeric approximation to the solution is often sufficient. The algorithms studied here can be used to compute such an approximation.

Standard deviation

In statistics, the standard deviation is a measure of the amount of variation or dispersion of a set of values. A low standard deviation indicates that the values tend to be close to the mean (also called the expected value) of the set, while a high standard deviation indicates that the values are spread out over a wider range. Standard deviation may be abbreviated SD, and is most commonly represented in mathematical texts and equations by the lower case Greek letter σ (sigma), for the population standard deviation, or the Latin letter s, for the sample standard deviation.

Explicit and implicit methods

Explicit and implicit methods are approaches used in numerical analysis for obtaining numerical approximations to the solutions of time-dependent ordinary and partial differential equations, as is required in computer simulations of physical processes. Explicit methods calculate the state of a system at a later time from the state of the system at the current time, while implicit methods find a solution by solving an equation involving both the current state of the system and the later one.

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