Approximation error

Polynomial approximations -- Calculus II

This lecture is on Calculus II. It follows Part II of the book Calculus Illustrated by Peter Saveliev. The text of the book can be found at http://calculus123.com.

From playlist Calculus II

Error bounds for Taylor approximations -- Calculus II

This lecture is on Calculus II. It follows Part II of the book Calculus Illustrated by Peter Saveliev. The text of the book can be found at http://calculus123.com.

From playlist Calculus II

What Are Error Intervals? GCSE Maths Revision

What are error Intervals and how do we find them - that's the mission in this episode of GCSE Maths minis! Error Intervals appear on both foundation and higher tier GCSE maths and IGCSE maths exam papers, so this is excellent revision for everyone! DOWNLOAD THE QUESTIONS HERE: https://d

From playlist Error Intervals & Bounds GCSE Maths Revision

Approximating Functions in a Metric Space

Approximations are common in many areas of mathematics from Taylor series to machine learning. In this video, we will define what is meant by a best approximation and prove that a best approximation exists in a metric space. Chapters 0:00 - Examples of Approximation 0:46 - Best Aproximati

From playlist Approximation Theory

Linear Algebra 6.6 Function Approximation; Fourier Series

My notes are available at http://asherbroberts.com/ (so you can write along with me). Elementary Linear Algebra: Applications Version 12th Edition by Howard Anton, Chris Rorres, and Anton Kaul A. Roberts is supported in part by the grants NSF CAREER 1653602 and NSF DMS 2153803.

From playlist Linear Algebra

Lecture: Higher-order Accuracy Schemes for Differentiation and Integration

The accuracy of the differentiation approximations is considered and new schemes are developed to lower the error. Integration is also introduced as a numerical algorithm.

From playlist Beginning Scientific Computing

Chapter 2 - Error and Percentage Error - IB Math Studies (Math SL)

Hello and welcome to What The Math. This is a Chapter 2 video about error and percentage error calculation. This is a part of Chapter 2. This is from Harris Publication version of IB math book by Haese.

From playlist IB Math Studies Chapter 2

Taylor polynomials -- Calculus II

This lecture is on Calculus II. It follows Part II of the book Calculus Illustrated by Peter Saveliev. The text of the book can be found at http://calculus123.com.

From playlist Calculus II

Proof of the Exact Error of Taylor Series!

A proof by induction of the integral form of the remainder of a Taylor series. Identities like Taylor's inequality that give bounds for the remainder are based on this integral! Check out more calculus problems: https://www.youtube.com/playlist?list=PLug5ZIRrShJGFne7YhMi-4eYsUKzkITao New

From playlist Calculus Problems

Epsilon delta definition of limit explained on an example

Epsilon delta definition of limit made easy

From playlist Summer of Math Exposition Youtube Videos

Minimax Approximation and the Exchange Algorithm

In this video we'll discuss minimax approximation. This is a method of approximating functions by minimisation of the infinity (uniform) norm. The exchange algorithm is an iterative method of finding the approximation which minimises the infinity norm. FAQ : How do you make these animatio

From playlist Approximation Theory

Genevieve Dusson - Error bounds for properties in planewave electronic structure calculations

Recorded 06 May 2022. Genevieve Dusson of the Université de Franche-Comté (Besançon), Laboratoire de Mathématiques, presents "Error bounds for properties in planewave electronic structure calculations" at IPAM's Large-Scale Certified Numerical Methods in Quantum Mechanics Workshop. Abstrac

From playlist 2022 Large-Scale Certified Numerical Methods in Quantum Mechanics

Numerical Differentiation with Finite Difference Derivatives

Approximating derivatives numerically is an important task in many areas of science and engineering, especially for simulating differential equations. In this video, I introduce several approaches to approximate derivatives using finite difference schemes. The error of each method is exp

From playlist Engineering Math: Differential Equations and Dynamical Systems

DeepMind x UCL RL Lecture Series - Approximate Dynamic Programming [10/13]

Research Scientist Diana Borsa introduces approximate dynamic programming, exploring what we can say theoretically about the performance of approximate algorithms. Slides: https://dpmd.ai/approximatedynamic Full video lecture series: https://dpmd.ai/DeepMindxUCL21

From playlist Learning resources

Calculus BC - Unit 5 Lesson 2: Lagrange Error Bound

Calculus BC - Taylor's Remainder Theorem and the Lagrange Error Bound

From playlist AP Calculus BC

Functions are Vectors? Fourier Series and an Illustration of the Why #SoME2

This is my submission to 3blue1brown's Summer of Math Exposition 2. Link to Khan Academy Video on Dot Product Linearity and Symmetry: https://www.khanacademy.org/math/linear-algebra/vectors-and-spaces/dot-cross-products/v/proving-vector-dot-product-properties Link to 3blue1brown Video on

From playlist Summer of Math Exposition 2 videos

Differentials Tangent Line Approximation Propagated Error Linearization

Tangent Line Approximation examples 13:52 17:04 24:42 , also called linearization. Calculating Differential Examples 24:14 at 28:50 I say that 1+2 is 2, but the final answer is correct, sorry for the minor error. Estimating Propagated Error Examples 29:17 35:42 41:57 50:16 Find free revie

From playlist Calculus

4.2 Linear Approximations and Differentials

OpenStax Calculus volume 1

From playlist Calculus 1

Tangent plane approximation and error estimation

Free ebook http://tinyurl.com/EngMathYT This lecture shows how to use tangent plane techniques to approximate complicated functions. We also discuss how to estimate the errors involved.

From playlist Mathematics for Finance & Actuarial Studies 2

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