Adaptive quadrature is a numerical integration method in which the integral of a function is approximated using static quadrature rules on adaptively refined subintervals of the region of integration. Generally, adaptive algorithms are just as efficient and effective as traditional algorithms for "well behaved" integrands, but are also effective for "badly behaved" integrands for which traditional algorithms may fail. (Wikipedia).

Adaptive Quadrature | Lecture 41 | Vector Calculus for Engineers

What is adaptive quadrature? Join me on Coursera: https://www.coursera.org/learn/numerical-methods-engineers Lecture notes at http://www.math.ust.hk/~machas/numerical-methods-for-engineers.pdf Subscribe to my channel: http://www.youtube.com/user/jchasnov?sub_confirmation=1

From playlist Numerical Methods for Engineers

Gaussian Quadrature | Lecture 40 | Numerical Methods for Engineers

An explanation of Gaussian quadrature. An example of how to calculate the weights and nodes for two-point Legendre-Gauss quadrature. Join me on Coursera: https://www.coursera.org/learn/numerical-methods-engineers Lecture notes at http://www.math.ust.hk/~machas/numerical-methods-for-engi

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Adding Vectors Geometrically: Dynamic Illustration

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This video introduces the concept of phased arrays. An array refers to multiple sensors, arranged in some configuration, that act together to produce a desired sensor pattern. With a phased array, we can electronically steer that pattern without having to physically move the array simply b

From playlist Understanding Phased Array Systems and Beamforming

Computational Methods for Numerical Relativity, Part 3 Frans Pretorius

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An introduction to Beamforming

This video talks about how we actually have more control over the shape of the beam than just adding additional elements or adjusting the position and orientation of the elements. We can also adjust the gain of the signal to each element and apply phase unevenly to each element, and that

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Engineering CEE 20: Engineering Problem Solving. Lecture 24

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From playlist Engineering CEE 20: Engineering Problem Solving

DDPS | Towards reliable, efficient, and automated model reduction of parametrized nonlinear PDEs

Description: Many engineering tasks, such as parametric study and uncertainty quantification, require rapid and reliable solution of partial differential equations (PDEs) for many different configurations. In this talk, we consider goal-oriented model reduction of parametrized nonlinear PD

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This video is #8 in the Adaptive Experimentation series presented at the 18th IEEE Conference on eScience in Salt Lake City, UT (October 10-14, 2022). In this video, Sterling Baird @sterling-baird presents on continuous multifidelity optimization. Continuous multi-fidelity optimization is

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Tzanio Kolev - Meso and Macroscale Modeling 1 - IPAM at UCLA

Recorded 15 March 2023. Tzanio Kolev of Lawrence Livermore National Laboratory presents "Meso and Macroscale Modeling 1" at IPAM's New Mathematics for the Exascale: Applications to Materials Science Tutorials. Learn more online at: http://www.ipam.ucla.edu/programs/workshops/new-mathematic

Lin Lin - Large scale hybrid DFT functionals: fast algorithms and finite-size effects - IPAM at UCLA

Recorded 02 May 2022. Lin Lin of the University of California, Berkeley, Mathematics, presents "Large scale hybrid DFT functionals: fast algorithms and finite-size effects" at IPAM's Large-Scale Certified Numerical Methods in Quantum Mechanics Workshop. Abstract: I will discuss recent prog

CMPSC/Math 451: Feb. 23, 2015. Adaptive Simpson's Rule. Wen Shen

Wen Shen, Penn State University. Lectures are based on my book: "An Introduction to Numerical Computation", published by World Scientific, 2016. See promo video: https://youtu.be/MgS33HcgA_I

Daniele Avitabile - Projection methods for neurobiological networks

---------------------------------- Institut Henri Poincaré, 11 rue Pierre et Marie Curie, 75005 PARIS http://www.ihp.fr/ Rejoingez les réseaux sociaux de l'IHP pour être au courant de nos actualités : - Facebook : https://www.facebook.com/InstitutHenriPoincare/ - Twitter : https://twitter

FFT based spectral Ewald methods as an alternative to multipole methods – A.-K. Tornberg – ICM2018

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Scott Field - Gravitational Wave Parameter Estimation with Compressed Likelihood Evaluations

Recorded 17 November 2021. Scott Field of the University of Massachusetts Dartmouth presents "Gravitational Wave Parameter Estimation with Compressed Likelihood Evaluations" at IPAM's Workshop III: Source inference and parameter estimation in Gravitational Wave Astronomy. Abstract: One of

Composite Quadrature Rules | Lecture 39 | Numerical Methods for Engineers

Composite quadrature rules (numerical integration) using the trapezoidal rule and Simpson's rule. Join me on Coursera: https://www.coursera.org/learn/numerical-methods-engineers Lecture notes at http://www.math.ust.hk/~machas/numerical-methods-for-engineers.pdf Subscribe to my channel:

From playlist Numerical Methods for Engineers