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Lecture

Continuous Training and Interpolation

In course

MATH-301: Ordinary differential equations

Ce cours donne une introduction rigoureuse au principaux thèmes de la théorie des équations différentielles ordinaires (EDO). Les EDO sont fondamentales pour l'étude des systèmes dynamiques et des équations aux dérivées partielles. Des applications dans ce ...

Description

This lecture covers the concept of continuous training and interpolation between functions, focusing on defining unique solutions and performing interpolation operations. The instructor explains the process step by step, introducing the idea of continuous formation and auxiliary problems. Various methods of interpolation between functions are discussed, emphasizing the importance of finding unique solutions to initial problems. The lecture concludes with a detailed explanation of continuous dependence and the family of solutions. Throughout the lecture, the instructor provides examples and practical applications to illustrate the theoretical concepts.

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Instructors (2)

François Samer Genoud

2000-2005 Etudes en Physique à l'EPFL dont une année d'échange à Zürich (ETH, 2002-2003). 2005 Diplôme de Physicien EPF (Master) en physique mathématique, sous la direction du Prof. C.-E. Pfister. 2005-2008 Doctorat sous la direction du Prof. C.A. Stuart, sur l'étude de certaines équations de Schrödinger non-linéaires, en particulier de questions liées à l'existence et la stabilité orbitale d'ondes stationnaires. 2009-2010 Chercheur postdoc FNS à OxPDE, Université d'Oxford. 2010-2013 Chercheur postdoc à l'Université Heriot-Watt, Edimbourg. 2013-2014 Chercheur postdoc à l'Université de Vienne. 2015-2017 Professeur Assistant à TU Delft. 2017-2018 Professeur Associé à l'Université libre de Bruxelles. 2018- Directeur du CMS Domaines d'intérêt scientifique: analyse non-linéaire, calcul des variations, équations aux dérivées partielles, physique mathématique et physique théorique en général.

Joachim Krieger

Official source

Ontological neighbourhood

Mathematics

Analysis: Numerical analysis

Related concepts (32)

Bilinear interpolation

In mathematics, bilinear interpolation is a method for interpolating functions of two variables (e.g., x and y) using repeated linear interpolation. It is usually applied to functions sampled on a 2D rectilinear grid, though it can be generalized to functions defined on the vertices of (a mesh of) arbitrary convex quadrilaterals. Bilinear interpolation is performed using linear interpolation first in one direction, and then again in another direction.

Polynomial interpolation

In numerical analysis, polynomial interpolation is the interpolation of a given bivariate data set by the polynomial of lowest possible degree that passes through the points of the dataset. Given a set of n + 1 data points , with no two the same, a polynomial function is said to interpolate the data if for each . There is always a unique such polynomial, commonly given by two explicit formulas, the Lagrange polynomials and Newton polynomials.

Multivariate interpolation

In numerical analysis, multivariate interpolation is interpolation on functions of more than one variable (multivariate functions); when the variates are spatial coordinates, it is also known as spatial interpolation. The function to be interpolated is known at given points and the interpolation problem consists of yielding values at arbitrary points . Multivariate interpolation is particularly important in geostatistics, where it is used to create a digital elevation model from a set of points on the Earth's surface (for example, spot heights in a topographic survey or depths in a hydrographic survey).

Interpolation

In the mathematical field of numerical analysis, interpolation is a type of estimation, a method of constructing (finding) new data points based on the range of a discrete set of known data points. In engineering and science, one often has a number of data points, obtained by sampling or experimentation, which represent the values of a function for a limited number of values of the independent variable. It is often required to interpolate; that is, estimate the value of that function for an intermediate value of the independent variable.

Trigonometric interpolation

In mathematics, trigonometric interpolation is interpolation with trigonometric polynomials. Interpolation is the process of finding a function which goes through some given data points. For trigonometric interpolation, this function has to be a trigonometric polynomial, that is, a sum of sines and cosines of given periods. This form is especially suited for interpolation of periodic functions. An important special case is when the given data points are equally spaced, in which case the solution is given by the discrete Fourier transform.

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