Numerical differential equations
In numerical analysis, adaptive mesh refinement (AMR) is a method of adapting the accuracy of a solution within certain sensitive or turbulent regions of simulation, dynamically and during the time the solution is being calculated. When solutions are calculated numerically, they are often limited to pre-determined quantified grids as in the Cartesian plane which constitute the computational grid, or 'mesh'. Many problems in numerical analysis, however, do not require a uniform precision in the numerical grids used for graph plotting or computational simulation, and would be better suited if specific areas of graphs which needed precision could be refined in quantification only in the regions requiring the added precision. Adaptive mesh refinement provides such a dynamic programming environment for adapting the precision of the numerical computation based on the requirements of a computation problem in specific areas of multi-dimensional graphs which need precision while leaving the other regions of the multi-dimensional graphs at lower levels of precision and resolution. This dynamic technique of adapting computation precision to specific requirements has been accredited to Marsha Berger, Joseph Oliger, and Phillip Colella who developed an algorithm for dynamic gridding called local adaptive mesh refinement. The use of AMR has since then proved of broad use and has been used in studying turbulence problems in hydrodynamics as well as in the study of large scale structures in astrophysics as in the Bolshoi Cosmological Simulation. (Wikipedia).
Computational Methods for Numerical Relativity, Part 3 Frans Pretorius
Computational Methods for Numerical Relativity, Part 3 Frans Pretorius Princeton University July 22, 2009
From playlist PiTP 2009
How To Build User-Adaptive Interfaces
Users have indicated many preferences on their devices these days. They want the operating system and apps to look and feel like their own. User-adaptive interfaces are those which are ready to use these preferences to enhance the user experience, to make it feel more at home. If done corr
From playlist Web Design: CSS / SVG
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
From playlist Understanding Phased Array Systems and Beamforming
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
Adaptive Fluid Simulations | Two Minute Papers #10
There are computer programs that can simulate the behavior of fluids, such as water, milk, honey and many others. However, creating detailed simulations takes a really long time, up to days even for a few seconds of video footage. Adaptive algorithms are a class of techniques that try to
From playlist Fluid, Cloth and Hair Simulations (Two Minute Papers)
Astronomy - Ch. 6: Telescopes (13 of 21) Adaptive Optics to Our Atmosphere
Visit http://ilectureonline.com for more math and science lectures! In this video I will explain how adaptive optics are use to counter our atmosphere.
From playlist ASTRONOMY 6 TELESCOPES
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
Raúl Tempone: Adaptive strategies for Multilevel Monte Carlo
Abstract: We will first recall, for a general audience, the use of Monte Carlo and Multi-level Monte Carlo methods in the context of Uncertainty Quantification. Then we will discuss the recently developed Adaptive Multilevel Monte Carlo (MLMC) Methods for (i) It Stochastic Differential Equ
From playlist Probability and Statistics
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
From playlist Data-driven Physical Simulations (DDPS) Seminar Series
Martin Vohralík: Adaptive inexact Newton methods and their application to multi-phase flows
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 Numerical Analysis and Scientific Computing
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
From playlist 2023 New Mathematics for the Exascale: Applications to Materials Science Tutorials
Martin Vohralik: A posteriori error estimates and solver adaptivity in numerical simulations
Abstract: We review how to bound the error between the unknown weak solution of a PDE and its numerical approximation via a fully computable a posteriori estimate. We focus on approximations obtained at an arbitrary step of a linearization (Newton-Raphson, fixed point, ...) and algebraic s
From playlist Numerical Analysis and Scientific Computing
MFEM Workshop 2021 | The State of MFEM
The LLNL-led MFEM (Modular Finite Element Methods) project provides high-order mathematical calculations for large-scale scientific simulations. The project’s first community workshop was held virtually on October 20, 2021, with participants around the world. Learn more about MFEM at https
From playlist MFEM Community Workshop 2021
Numerical Homogenization by Localized Orthogonal Decomposition (Lecture 3) by Daniel Peterseim
DISCUSSION MEETING Multi-Scale Analysis: Thematic Lectures and Meeting (MATHLEC-2021, ONLINE) ORGANIZERS: Patrizia Donato (University of Rouen Normandie, France), Antonio Gaudiello (Università degli Studi di Napoli Federico II, Italy), Editha Jose (University of the Philippines Los Baño
From playlist Multi-scale Analysis: Thematic Lectures And Meeting (MATHLEC-2021) (ONLINE)
MFEM Workshop 2022 | High-Order Solvers + GPU Acceleration
The LLNL-led MFEM (Modular Finite Element Methods) project provides high-order mathematical calculations for large-scale scientific simulations. The project’s second community workshop was held on October 25, 2022, with participants around the world. Learn more about MFEM at https://mfem.o
From playlist MFEM Community Workshop 2022
What is Curve Fitting Toolbox? - Curve Fitting Toolbox Overview
Get a Free Trial: https://goo.gl/C2Y9A5 Get Pricing Info: https://goo.gl/kDvGHt Ready to Buy: https://goo.gl/vsIeA5 Fit curves and surfaces to data using regression, interpolation, and smoothing using Curve Fitting Toolbox. For more videos, visit http://www.mathworks.com/products/curvefi
From playlist Math, Statistics, and Optimization
FEM@LLNL | Computing Meets Cardiology: Making Heart Simulations Fast and Accurate
Sponsored by the MFEM project, the FEM@LLNL Seminar Series focuses on finite element research and applications talks of interest to the MFEM community. On September 13, 2022, Dennis Ogiermann of the University of Bochum presented "Computing Meets Cardiology: Making Heart Simulations Fast
From playlist FEM@LLNL Seminar Series
Stanford Seminar - Rethinking Memory System Design for Data-Intensive Computing
"Rethinking Memory System Design for Data-Intensive Computing"- Onur Mutlu of Carnegie Mellon University About the talk: The memory system is a fundamental performance and energy bottleneck in almost all computing systems. Recent system design, application, and technology trends that requ
From playlist Engineering
Wolfram Physics Project: Solving the Einstein Equations & Other PDEs Tuesday, Mar. 9, 2021
This is a Wolfram Physics Project working session on solving the Einstein equations and other PDE's in the Wolfram Model. Begins at 1:18 Originally livestreamed at: https://twitch.tv/stephen_wolfram Stay up-to-date on this project by visiting our website: http://wolfr.am/physics Check ou
From playlist Wolfram Physics Project Livestream Archive