The Mathieu equation is a linear second-order differential equation with periodic coefficients. The French mathematician, E. Léonard Mathieu, first introduced this family of differential equations, nowadays termed Mathieu equations, in his “Memoir on vibrations of an elliptic membrane” in 1868. "Mathieu functions are applicable to a wide variety of physical phenomena, e.g., diffraction, amplitude distortion, inverted pendulum, stability of a floating body, radio frequency quadrupole, and vibration in a medium with modulated density" (Wikipedia).
Stéphane Jaffard: From function spaces to stylometry: the wavelet bridge
Find this video and other talks given by worldwide mathematicians on CIRM's Audiovisual Mathematics Library: http://library.cirm-math.fr. And discover all its functionalities: - Chapter markers and keywords to watch the parts of your choice in the video - Videos enriched with abstracts, b
From playlist 30 years of wavelets
An introduction to the wavelet transform (and how to draw with them!)
The wavelet transform allows to change our point of view on a signal. The important information is condensed in a smaller space, allowing to easily compress or filter the signal. A lot of approximations are made in this video, like a lot of missing √2 factors. This choice was made to keep
From playlist Summer of Math Exposition Youtube Videos
Wavelets: a mathematical microscope
Wavelet transform is an invaluable tool in signal processing, which has applications in a variety of fields - from hydrodynamics to neuroscience. This revolutionary method allows us to uncover structures, which are present in the signal but are hidden behind the noise. The key feature of w
From playlist Fourier
Journées Hénon - 10/21 - Thibault Damour
Chaos in classical and quantum cosmological billiards
From playlist Michel Hénon Memoriam
Inauguration de la Maison de chercheurs Jean-Morlet
La Maison de chercheurs Jean-Morlet a été inaugurée au Centre International de rencontres Mathématiques le vendredi 22 mars 2013. Vous pourrez entendre dans ce film les discours de: Patrick Foulon, directeur du CIRM Dominique Léger, Vice-président de la Fondation du Patrimoine, François T
From playlist Interviews en français - French interviews
Stéphane Mallat: "Deep Generative Networks as Inverse Problems"
New Deep Learning Techniques 2018 "Deep Generative Networks as Inverse Problems" Stéphane Mallat, École Normale Supérieure Abstract: Generative Adversarial Networks and Variational Auto-Encoders provide impressive image generations from Gaussian white noise, which are not well understood
From playlist New Deep Learning Techniques 2018
Learning Multiscale Physics with Deep Neural Networks - Mallat - Workshop 2 - CEB T3 2018
Stephane Mallat (College de France/ENS) / 26.10.2018 Learning Multiscale Physics with Deep Neural Networks ---------------------------------- Vous pouvez nous rejoindre sur les réseaux sociaux pour suivre nos actualités. Facebook : https://www.facebook.com/InstitutHenriPoincare/ Twitte
From playlist 2018 - T3 - Analytics, Inference, and Computation in Cosmology
Hajar Bahouri - De l'analyse des EDPs non linéaires à la théorie des groupes
Créteil, Prix Paul Doisteau 2016 Réalisation technique : Antoine Orlandi (GRICAD) | Tous droits réservés
From playlist Des mathématiciens primés par l'Académie des Sciences 2017
Pierre de Fermat: Biography of a Great Thinker
As a mathematician, Pierre de Fermat is known as the "Prince of Amateurs." He was actually a lawyer, but worked on math as a hobby. He helped found probability theory and analytic geometry, and made many contributions to number theory. He is possibly best known for "Fermat's Last Theore
From playlist It Starts With Literacy
Jean-Claude Risset: Sound, music and wavelets in Marseille: A reminder of early sonic [...]
Find this video and other talks given by worldwide mathematicians on CIRM's Audiovisual Mathematics Library: http://library.cirm-math.fr. And discover all its functionalities: - Chapter markers and keywords to watch the parts of your choice in the video - Videos enriched with abstracts, b
From playlist 30 years of wavelets
Pascal HUBERT est mathématicien, professeur au sein d'Aix-Marseille Université et directeur de la FRUMAM. Il parle ici de son grand-père, qui lui a donné le goût des mathématiques, de ses recherches, de la richesse mathématique marseillaise, de sa collaboration avec Artur Avila (Médaille
From playlist Interviews en français - French interviews
Understanding Wavelets, Part 1: What Are Wavelets
This introductory video covers what wavelets are and how you can use them to explore your data in MATLAB®. •Try Wavelet Toolbox: https://goo.gl/m0ms9d •Ready to Buy: https://goo.gl/sMfoDr The video focuses on two important wavelet transform concepts: scaling and shifting. The concepts ca
From playlist Understanding Wavelets
Glen Evenbly: "Using tensor networks to design improved wavelets for image compression"
Tensor Methods and Emerging Applications to the Physical and Data Sciences 2021 Workshop II: Tensor Network States and Applications "Using tensor networks to design improved wavelets for image compression" Glen Evenbly - Georgia Institute of Technology, Physics Abstract: Tensor networks
From playlist Tensor Methods and Emerging Applications to the Physical and Data Sciences 2021
Wavelets and Multiresolution Analysis
This video discusses the wavelet transform. The wavelet transform generalizes the Fourier transform and is better suited to multiscale data. Book Website: http://databookuw.com Book PDF: http://databookuw.com/databook.pdf These lectures follow Chapter 2 from: "Data-Driven Science an
From playlist Data-Driven Science and Engineering
Morlet wavelets in time and in frequency
Convolution requires two time series: The data and the kernel. The data is what you already have (EEG/MEG/LFP/etc); here you will learn about the most awesomest kernel for time-frequency decomposition of neural time series data: The Morlet wavelet. The video uses files you can download fr
From playlist OLD ANTS #3) Time-frequency analysis via Morlet wavelet convolution
Stéphane Mallat: A Wavelet Zoom to Analyze a Multiscale World
Abstract: Complex physical phenomena, signals and images involve structures of very different scales. A wavelet transform operates as a zoom, which simplifies the analysis by separating local variations at different scales. Yves Meyer found wavelet orthonormal bases having better propertie
From playlist Abel Lectures
Understanding Wavelets, Part 2: Types of Wavelet Transforms
Explore the workings of wavelet transforms in detail. •Try Wavelet Toolbox: https://goo.gl/m0ms9d •Ready to Buy: https://goo.gl/sMfoDr You will also learn important applications of using wavelet transforms with MATLAB®. Video Transcript: In the previous session, we discussed wavelet co
From playlist Understanding Wavelets
Effects of Morlet wavelet parameters on results
There is one important parameter of Morlet wavelets, which is the width of the Gaussian (a.k.a. the "number of cycles"). In this video we will explore this parameter and see what effects different parameter values have on the results. I will also provide some advice for when you should use
From playlist OLD ANTS #3) Time-frequency analysis via Morlet wavelet convolution