Integral equations

Marchenko equation

In mathematical physics, more specifically the one-dimensional inverse scattering problem, the Marchenko equation (or Gelfand-Levitan-Marchenko equation or GLM equation), named after Israel Gelfand, Boris Levitan and Vladimir Marchenko, is derived by computing the Fourier transform of the scattering relation: Where is a symmetric kernel, such that which is computed from the scattering data. Solving the Marchenko equation, one obtains the kernel of the transformation operator from which the potential can be read off. This equation is derived from the , using the . (Wikipedia).

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B25 Example problem solving for a Bernoulli equation

See how to solve a Bernoulli equation.

From playlist Differential Equations

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Bernoulli ode

Illustrates the solution of a Bernoulli first-order differential equation. Free books: http://bookboon.com/en/differential-equations-with-youtube-examples-ebook http://www.math.ust.hk/~machas/differential-equations.pdf

From playlist Differential Equations with YouTube Examples

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Introduction to Parametric Equations

This video defines a parametric equations and shows how to graph a parametric equation by hand. http://mathispower4u.yolasite.com/

From playlist Parametric Equations

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Riccati ode

Illustrates the solution of a Riccati first-order differential equation. Free books: http://bookboon.com/en/differential-equations-with-youtube-examples-ebook http://www.math.ust.hk/~machas/differential-equations.pdf

From playlist Differential Equations with YouTube Examples

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The Role of the Transpose in Free Probability - J.Mingo - Workshop 2 - CEB T3 2017

James Mingo / 26.10.17 The Role of the Transpose in Free Probability: the partial transpose of R-cyclic operators Like tensor independence, free independence gives us rules for doing calculations. With random matrix models, we usually need tensor independence of the entries and some kin

From playlist 2017 - T3 - Analysis in Quantum Information Theory - CEB Trimester

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Iain Johnstone: Eigenvalues and variance components

Abstract: Motivated by questions from quantitative genetics, we consider high dimensional versions of some common variance component models. We focus on quadratic estimators of 'genetic covariance' and study the behavior of both the bulk of the estimated eigenvalues and the largest estimat

From playlist Probability and Statistics

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B24 Introduction to the Bernoulli Equation

The Bernoulli equation follows from a linear equation in standard form.

From playlist Differential Equations

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How to determine if an equation is a linear relation

👉 Learn how to determine if an equation is a linear equation. A linear equation is an equation whose highest exponent on its variable(s) is 1. The variables do not have negative or fractional, or exponents other than one. Variables must not be in the denominator of any rational term and c

From playlist Write Linear Equations

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Sandrine Péché: Eigenvalue distribution for non linear models of random matrices

The talk concerned with the asymptotic empirical eigenvalue distribution of a non linear random matrix ensemble. More precisely we consider $M= \frac{1}{m} YY^*$ with $Y=f(WX)$ where W and X are random rectangular matrices with i.i.d. centered entries. The function f is applied pointwise

From playlist Probability and Statistics

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Tracy-Widom at each edge of real covariance and MANOVA estimators by Zhou Fan

PROGRAM :UNIVERSALITY IN RANDOM STRUCTURES: INTERFACES, MATRICES, SANDPILES ORGANIZERS :Arvind Ayyer, Riddhipratim Basu and Manjunath Krishnapur DATE & TIME :14 January 2019 to 08 February 2019 VENUE :Madhava Lecture Hall, ICTS, Bangalore The primary focus of this program will be on the

From playlist Universality in random structures: Interfaces, Matrices, Sandpiles - 2019

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Solve a Bernoulli Differential Equation Initial Value Problem

This video provides an example of how to solve an Bernoulli Differential Equations Initial Value Problem. The solution is verified graphically. Library: http://mathispower4u.com

From playlist Bernoulli Differential Equations

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Michael Mahoney: "Why Deep Learning Works: Implicit Self-Regularization in Deep Neural Networks"

Machine Learning for Physics and the Physics of Learning 2019 Workshop II: Interpretable Learning in Physical Sciences "Why Deep Learning Works: Implicit Self-Regularization in Deep Neural Networks" Michael Mahoney, University of California, Berkeley (UC Berkeley) Abstract: Physics has a

From playlist Machine Learning for Physics and the Physics of Learning 2019

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Calculus 2: Parametric Equations (1 of 20) What is a Parametric Equation?

Visit http://ilectureonline.com for more math and science lectures! In this video I will explain what is a parametric equation. A parametric equation is an equation that expresses each variable of an equation in terms of another variable. Next video in the series can be seen at: https://

From playlist CALCULUS 2 CH 17 PARAMETRIC EQUATIONS

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Summary for graph an equation in Standard form

👉 Learn about graphing linear equations. A linear equation is an equation whose highest exponent on its variable(s) is 1. i.e. linear equations has no exponents on their variables. The graph of a linear equation is a straight line. To graph a linear equation, we identify two values (x-valu

From playlist ⚡️Graph Linear Equations | Learn About

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Probability and Statistics

For more training resources, visit: http://www.wolfram.com/training/ See how easy it is to use the Wolfram Language to solve real-world statistics and probability problems with quantity data, enhanced time series support, and over 150 distributions, including random matrices. Notebook li

From playlist New in the Wolfram Language and Mathematica Version 11

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Eigenvalue bounds on sums of random matrices - Adam Marcus

Members’ Seminar Topic:Eigenvalue bounds on sums of random matrices Speaker: Adam Marcus Affilation: Princeton University Date: November 14, 2016 For more videos, visit http://video.ias.edu

From playlist Mathematics

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Catherine Sulem: Soliton Resolution for Derivative NLS equation

Abstract: We consider the Derivative Nonlinear Schrödinger equation for general initial conditions in weighted Sobolev spaces that can support bright solitons (but exclude spectral singularities). We prove global wellposedness and give a full description of the long-time behavior of the s

From playlist Women at CIRM

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Learn how to eliminate the parameter with trig functions

Learn how to eliminate the parameter in a parametric equation. A parametric equation is a set of equations that express a set of quantities as explicit functions of a number of independent variables, known as parameters. Eliminating the parameter allows us to write parametric equation in r

From playlist Parametric Equations

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Nexus Trimester - Gregory Valiant (Stanford) 2/2

When your big data seems too small: accurate inferences beyond the empirical distribution 2/2 Gregory Valiant (Stanford) March 14, 2016 Abstract: We discuss three problems related to the general challenge of making accurate inferences about a complex distribution, in the regime in which

From playlist 2016-T1 - Nexus of Information and Computation Theory - CEB Trimester

Related pages

Lax pair | Israel Gelfand | Boris Levitan | Fourier transform | Inverse scattering problem