Lecture: Numerical Differentiation Methods
From simple Taylor series expansions, the theory of numerical differentiation is developed.
From playlist Beginning Scientific Computing
Using first and second derivatives to sketch the graph of a function.
From playlist Differentiation
Ex: Using Differentials to Approximate the Value of a Cube Root.
This video provides an example of how differentials can be used to approximate the value of a cube root. Complete video library at www.mathispower4u.com
From playlist Differentiation Application - Differentials and Tangent Line Approximations
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
Implicit Differentiation (Differentiating a function without needing to rearrange for x or y)
More resources available at www.misterwootube.com
From playlist Applications of Calculus
Differentiation _ Explaining Differentiation.mov
Explains the connection between a limit, differentiation, and distance and velocity in classical mechanics.
From playlist Differentiation
DIFFERENTIATION - AS Level Maths
Well to the DIFFERENTIATION chapter for the AS Level Maths new specification (2017)! DIFFERENTIATION AS Level Maths: https://www.youtube.com/playlist?list=PLN6Lp_3e9iW3TlaCRzx6a6dig2eZdqGC8 Differentiation is our first introduction to calculus at AS Level and A Level Maths. Differentiat
From playlist AS Level Maths Pure - AQA, Edexcel and OCR MEI
Calculus - Application of Differentiation (17 of 60) Graph f(x)=x^4-4x^3 Using 1st & 2nd Derivatives
Visit http://ilectureonline.com for more math and science lectures! In this video I will graph f(x)=x^4-4x^3 using first and second derivatives.
From playlist CALCULUS 1 CH x APPLICATIONS OF DIFFERENTIATION
AP Calculus AB and BC Unit 2 Review [Differentiation: Definition and Basic Derivative Rules]
► My AP Calculus AB and BC Ultimate Review Packets: AB: https://bit.ly/KristaAB BC: https://bit.ly/KristaBC Before you watch this video all about Unit 2 of AP Calculus AB/BC, Differentiation: Definition and Basic Derivative Rules, make sure you get the study guide that goes with it. The s
From playlist AP Calculus BC
Smooth Transition Function in One Dimension | Smooth Transition Function Part 1
#SoME2 This video gives a detailed construction of transition function for various levels of smoothness. Sketch of proofs for 4 theorems regarding smoothness: https://kaba.hilvi.org/homepage/blog/differentiable.htm Faà di Bruno's formula: https://en.wikipedia.org/wiki/Fa%C3%A0_di_Bruno%2
From playlist Summer of Math Exposition 2 videos
A geometric integration approach to non-smooth (...) - Schoenlieb/Riis - Workshop 1 - CEB T1 2019
Schoenlieb/Riis (University of Cambridge) / 04.02.2019 A geometric integration approach to non-smooth and non-convex optimisation The optimisation of nonsmooth, nonconvex functions without access to gradients is a particularly challenging problem that is frequently encountered, for exam
From playlist 2019 - T1 - The Mathematics of Imaging
Lecture 17: Discrete Curvature II (Discrete Differential Geometry)
Full playlist: https://www.youtube.com/playlist?list=PL9_jI1bdZmz0hIrNCMQW1YmZysAiIYSSS For more information see http://geometry.cs.cmu.edu/ddg
From playlist Discrete Differential Geometry - CMU 15-458/858
ML Tutorial: Probabilistic Numerical Methods (Jon Cockayne)
Machine Learning Tutorial at Imperial College London: Probabilistic Numerical Methods Jon Cockayne (University of Warwick) February 22, 2017
From playlist Machine Learning Tutorials
Yat Tin Chow: "A numerical method of solving high dimensional Hamilton-Jacobi equations with gen..."
High Dimensional Hamilton-Jacobi PDEs 2020 Workshop I: High Dimensional Hamilton-Jacobi Methods in Control and Differential Games "A numerical method of solving high dimensional Hamilton-Jacobi equations with generalized Hopf-Lax formula" Yat Tin Chow - University of California, Riverside
From playlist High Dimensional Hamilton-Jacobi PDEs 2020
22nd Imaging & Inverse Problems (IMAGINE) OneWorld SIAM-IS Virtual Seminar Series Talk
Date: Wednesday, April 28, 2021, 10:00am Eastern Time Zone (US & Canada) Speaker: Sung Ha Kang, Georgia Tech Title: Vectorization, Decomposition and PDE identification Abstract: This talk covers a few problems in imaging and inverse problems: image vectorization, image decomposition and
From playlist Imaging & Inverse Problems (IMAGINE) OneWorld SIAM-IS Virtual Seminar Series
The computational theory of Riemann–Hilbert problems (Lecture 4) by Thomas Trogdon
Program : Integrable Systems in Mathematics, Condensed Matter and Statistical Physics ORGANIZERS : Alexander Abanov, Rukmini Dey, Fabian Essler, Manas Kulkarni, Joel Moore, Vishal Vasan and Paul Wiegmann DATE & TIME : 16 July 2018 to 10 August 2018 VENUE : Ramanujan Lecture Hall, ICT
From playlist Integrable systems in Mathematics, Condensed Matter and Statistical Physics
Terry Lyons: Modelling Diffusive Systems
This lecture was held at The University of Oslo, May 24, 2007 and was part of the Abel Prize Lectures in connection with the Abel Prize Week celebrations. Program for the Abel Lectures 2007 1. “A Short History of Large Deviations” by Srinivasa Varadhan, Abel Laureate 2007, Courant
From playlist Abel Lectures
Differentiation Techniques: Power Rule
This video introduces the constant and power rule of differentiation. This is an update of https://youtu.be/e1GMC9aOyBU which was missing a "2" in the denominator at 8:01. Website: mathispower4u.com
From playlist Differentiation of Basic Functions and Using the Power Rule
Lars Ruthotto: "A Numerical Analysis Perspective on Deep Neural Networks"
Machine Learning for Physics and the Physics of Learning 2019 Workshop I: From Passive to Active: Generative and Reinforcement Learning with Physics "A Numerical Analysis Perspective on Deep Neural Networks" Lars Ruthotto - Emory University Abstract: In this talk, I illustrate the use of
From playlist Machine Learning for Physics and the Physics of Learning 2019