ME564 Lecture 16: Numerical integration and numerical solutions to ODEs

preview_player
Показать описание
ME564 Lecture 16
Engineering Mathematics at the University of Washington

Numerical integration and numerical solutions to ODEs

Matlab code:

  1. Steve Brunton
  2. Engineering mathematics
  3. Mathematics
  4. Applied mathematics
  5. Matlab
  6. Calculus
Рекомендации по теме
ME564 Lecture 16: 0:46:33

ME564 Lecture 16: Numerical integration and numerical solutions to ODEs

ME564 Lecture 15: 0:48:37

ME564 Lecture 15: Numerical differentiation and numerical integration

ME 564 Lecture 0:18:51

ME 564 Lecture 16 Part 1

ME564: Lecture16 - 0:08:47

ME564: Lecture16 - Part 1

ME564 Lecture 17: 0:50:24

ME564 Lecture 17: Numerical solutions to ODEs (Forward and Backward Euler)

(Sept 16) Class 1:18:54

(Sept 16) Class 10- Numerical Methods: Chapter-5 : Numerical Differentiation and Integration

Numerical Integration: Discrete 0:29:43

Numerical Integration: Discrete Riemann Integrals and Trapezoid Rule

Numerical Integration 0:29:04

Numerical Integration

ME564 Lecture 14: 0:49:30

ME564 Lecture 14: Numerical differentiation using finite difference

Introduction to Numerical 7:36:23

Introduction to Numerical Integration (Free Full Course)

ME564: Lecture16 - 0:07:38

ME564: Lecture16 - Part 3

ME564: Lecture16 - 0:11:55

ME564: Lecture16 - Part 2

Introduction to Numerical 0:32:31

Introduction to Numerical Integration

Numerical Integration Crash 1:00:14

Numerical Integration Crash Course: All You Ever Might Need to Know in One Hour (Numerical Methods)

Runge-Kutta Integrator Overview: 0:30:22

Runge-Kutta Integrator Overview: All Purpose Numerical Integration of Differential Equations

ME564 Lecture 18: 0:48:57

ME564 Lecture 18: Runge-Kutta integration of ODEs and the Lorenz equation

ME564 Lecture 11: 0:50:15

ME564 Lecture 11: Degenerate systems of equations and non-normal energy growth

ME564 Lecture 13: 0:49:52

ME564 Lecture 13: ODEs with external forcing (inhomogeneous ODEs) and the convolution integral

Lecture 12 Numerical 0:49:23

Lecture 12 Numerical Differentiation - 3 Operator Method Numerical Integration - 1

Lecture 2021 Numerical 0:57:50

Lecture 2021 Numerical Methods: Session 24: Time-Discretisation of Stochastic Processes 1

ME564 Lecture 12: 0:49:36

ME564 Lecture 12: ODEs with external forcing (inhomogeneous ODEs)

Lecture -- Introduction 0:06:35

Lecture -- Introduction to Numerical Integration and Discrete Integration

Numerical Differentiation & 0:16:54

Numerical Differentiation & Integration Part 6 Numerical Differentiation Using Finite Difference

Analysis 1A - 0:33:27

Analysis 1A - Rose - Lecture #38 - Error Bounds for Numerical Integration - Part 1 of 3

Комментарии
Автор

The best definition of "implicitly defined" ever!!! :))))

michaeldevilangel
Автор

Why the error is proportional to ∆x^2 I think it's proportional to just the value of ∆x

bffnjlk
Автор

Why is the trapezoidal integral better? I can see this on a rectangle/trazoid by rectangle/trapezoid basis, but if you integrate from a to b, every f_i gets evaluated twice, so the total sum should be the same, thus the same error of magnitude of the integral, right?

hpfeichtinger