# Learn Differential Equations: Tutorials with Gilbert Strang and Cleve Moler

## Video Lectures

Displaying all 68 video lectures.

Lecture 1Play Video |
Introduction to Differential Equations and the MATLAB® ODE SuiteGilbert Strang and Cleve Moler provide an overview to their in-depth video series about differential equations and the MATLAB® ODE suite. |

Lecture 2Play Video |
Overview of Differential EquationsDifferential equations connect the slope of a graph to its height. Slope = height, slope = -height, slope = 2t times height: all linear. Slope = (height)^2 is nonlinear. |

Lecture 3Play Video |
The Calculus You NeedThe sum rule, product rule, and chain rule produce new derivatives from known derivatives. The Fundamental Theorem of Calculus says that the integral inverts the derivative. |

Lecture 4Play Video |
Response to Exponential InputFor a linear equation with exponential input from outside and exponential growth from inside, the solution is a combination of two exponentials. |

Lecture 5Play Video |
Response to Oscillating InputA linear equation with oscillating input has an oscillating output with the same frequency (and a phase shift). |

Lecture 6Play Video |
Solution for Any InputTo solve a linear first order equation, multiply each input by its growth factor and integrate those outputs. |

Lecture 7Play Video |
Step Function and Delta FunctionA unit step function jumps from 0 to 1. Its slope is a delta function: zero everywhere except infinite at the jump. |

Lecture 8Play Video |
Response to Complex ExponentialFor linear equations, the solution for a cosine input is the real part of the solution for a complex exponential input. That complex solution has magnitude G (the gain). |

Lecture 9Play Video |
Integrating Factor for Constant RateThe integrating factor multiplies the differential equation to allow integration. |

Lecture 10Play Video |
Integrating Factor for a Varying RateThe integral of a varying interest rate provides the exponent in the growing solution (the bank balance). |

Lecture 11Play Video |
The Logistic EquationWhen competition slows down growth and makes the equation nonlinear, the solution approaches a steady state. |

Lecture 12Play Video |
The Stability and Instability of Steady StatesSteady state solutions can be stable or unstable â€“ a simple test decides. |

Lecture 13Play Video |
Separable EquationsSeparable equations can be solved by two separate integrations, one in t and the other in y. |

Lecture 14Play Video |
Second Order EquationsFor the oscillation equation with no damping and no forcing, all solutions share the same natural frequency. |

Lecture 15Play Video |
Forced Harmonic MotionWhen the forcing is a sinusoidal input, like a cosine, one particular solution has the same form. But if the forcing frequency equals the natural frequency there is resonance. |

Lecture 16Play Video |
Unforced Damped MotionWith constant coefficients in a differential equation, the basic solutions are exponentials. The exponent solves a simple equation. |

Lecture 17Play Video |
Impulse Response and Step ResponseThe impulse response is the solution when the force is an impulse (a delta function). This also solves a null equation (no force) with a nonzero initial condition. |

Lecture 18Play Video |
Exponential Response — Possible ResonanceResonance occurs when the natural frequency matches the forcing frequency — equal exponents from inside and outside. |

Lecture 19Play Video |
Second Order Equations with DampingA damped forced equation has a sinusoidal solution with exponential decay. The damping ratio provides insight into the null solutions. |

Lecture 20Play Video |
Electrical Networks: Voltages and CurrentsCurrent flowing around an RLC loop solves a linear equation with coefficients L (inductance), R (resistance), and 1/C (C = capacitance). |

Lecture 21Play Video |
Method of Undetermined CoefficientsWith constant coefficients and special forcing terms (powers of t, cosines/sines, exponentials), a particular solution has this same form. |

Lecture 22Play Video |
An Example of Undetermined CoefficientsThis method is also successful for forces and solutions equal to polynomials times exponentials. Substitute into the equation! |

Lecture 23Play Video |
Variation of ParametersCombine null solutions to find a particular solution for any right hand side. But it may involve a difficult integral. |

Lecture 24Play Video |
Laplace Transform: First Order EquationTransform each term in the linear differential equation to create an algebra problem. You can transform the algebra solution back to the ODE solution. |

Lecture 25Play Video |
Laplace Transform: Second Order EquationThe algebra problem involves the transfer function. The poles of that function are all-important. |

Lecture 26Play Video |
Laplace Transforms and ConvolutionWhen the input force is an impulse, the output is the impulse response. For all inputs the response is a ""convolution"" with the impulse response. |

Lecture 27Play Video |
Pictures of SolutionsThe direction field has an arrow with slope at each point coming from the differential equation. Arrows with the same slope lie along an ""isocline"". |

Lecture 28Play Video |
Phase Plane Pictures: Source, Sink, SaddleSolutions to second order equations can approach infinity or zero. Saddle points have a positive and also a negative exponent or eigenvalue. |

Lecture 29Play Video |
Phase Plane Pictures: Spirals and CentersImaginary exponents with pure oscillation provide a ""center"" in the phase plane. The point (position, velocity) travels forever around an ellipse. |

Lecture 30Play Video |
Two First Order Equations: StabilityA second order equation gives two first order equations. The matrix becomes a companion matrix (triangular). |

Lecture 31Play Video |
Linearization at Critical PointsA critical point is a constant solution to the differential equation. The slope of the right hand side decides stability or instability. |

Lecture 32Play Video |
Linearization of Two Nonlinear EquationsWith two equations, the two linearized equations use the 2 by 2 matrix of partial derivatives of the right hand sides. |

Lecture 33Play Video |
Eigenvalues and Stability: 2 by 2 Matrix, ATwo equations with a constant matrix are stable (solutions approach zero) when the trace is negative and the determinant is positive. |

Lecture 34Play Video |
The Tumbling Box in 3-DA box in the air can rotate stably around its shortest and longest axes. Around the middle axis it tumbles wildly. |

Lecture 35Play Video |
The Column Space of a MatrixCapturing all combinations of the columns gives the column space of the matrix. It is a subspace (such as a plane). |

Lecture 36Play Video |
Independence, Basis, and DimensionVectors are a basis for a subspace if their combinations span the whole subspace and are independent: no basis vector is a combination of the others. Dimension = number of basis vectors. |

Lecture 37Play Video |
The Big Picture of Linear AlgebraA matrix produces four subspaces: column space, row space (same dimension), the space of vectors perpendicular to all rows (the nullspace), and the space of vectors perpendicular to all columns. |

Lecture 38Play Video |
GraphsA graph has nodes connected by edges (other edges can be missing). This is a useful model for the Internet, the brain, pipeline systems, and much more. |

Lecture 39Play Video |
Incidence Matrices of GraphsThe incidence matrix has a row for every edge, containing -1 and +1 to show which two nodes are connected by that edge. |

Lecture 40Play Video |
Eigenvalues and EigenvectorsThe eigenvectors remain in the same direction when multiplied by the matrix. Subtracting an eigenvalue from the diagonal leaves a singular matrix: determinant zero. An n by n matrix has n eigenvalues. |

Lecture 41Play Video |
Diagonalizing a MatrixA matrix can be diagonalized if it has n independent eigenvectors. The diagonal matrix Î› is the eigenvalue matrix. |

Lecture 42Play Video |
Powers of Matrices and Markov MatricesDiagonalizing a matrix also diagonalizes all its powers. |

Lecture 43Play Video |
Solving Linear SystemsAn eigenvalue / eigenvector pair leads to a solution to a constant coefficient system of differential equations. Combinations of those solutions lead to all solutions. |

Lecture 44Play Video |
The Matrix ExponentialThe shortest form of the solution uses the matrix exponential multiplying the starting vector (the initial condition). |

Lecture 45Play Video |
Similar MatricesIf A and B are ""similar"" then B has the same eigenvalues as A. |

Lecture 46Play Video |
Symmetric Matrices, Real Eigenvalues, Orthogonal EigenvectorsSymmetric matrices have n perpendicular eigenvectors and n real eigenvalues. |

Lecture 47Play Video |
Second Order SystemsAn oscillation equation has 2n solutions, n cosines and n sines. Those solutions use the eigenvectors and eigenvalues. |

Lecture 48Play Video |
Positive Definite MatricesA positive definite matrix has positive eigenvalues, positive pivots, positive determinants, and positive energy. |

Lecture 49Play Video |
Singular Value Decomposition (the SVD)The SVD factors each matrix into an orthogonal matrix times a diagonal matrix (the singular value) times another orthogonal matrix: rotation times stretch times rotation. |

Lecture 50Play Video |
Boundary Conditions Replace Initial ConditionsA second order equation can change from two initial conditions to boundary conditions at two points. |

Lecture 51Play Video |
Laplace EquationLaplace's partial differential equation describes temperature distribution inside a circle or a square or any plane region. |

Lecture 52Play Video |
Fourier SeriesA Fourier series separates a periodic function into a combination (infinite) of all cosine and since basis functions. |

Lecture 53Play Video |
Examples of Fourier SeriesEven functions use only cosines and odd functions use only sines. The coefficients in the Fourier series come from integrals. |

Lecture 54Play Video |
Fourier Series Solution of Laplace's EquationAround every circle, the solution to Laplaceâ€™s equation is a Fourier series with coefficients proportional to r^n. On the boundary circle, the given boundary values determine those coefficients. |

Lecture 55Play Video |
Heat EquationThe heat equation starts from a temperature distribution at t = 0 and follows it as it quickly becomes smooth. |

Lecture 56Play Video |
Wave EquationThe wave equation shows how waves move along the x axis, starting from a given wave shape and its velocity. There can be fixed endpoints as with a violin string. |

Lecture 57Play Video |
Euler, ODE1Instructor: Cleve Moler ODE1 implements Euler's method. It provides an introduction to numerical methods for ODEs and to the MATLAB suite of ODE solvers. Exponential growth and compound interest are used as examples. |

Lecture 58Play Video |
Midpoint Method, ODE2Instructor: Cleve Moler ODE2 implements a midpoint method with two function evaluations per step. This method is twice as accurate as Euler's method. A nonlinear equation defining the sine function provides an example. An exercise involves implementing a trapezoid method. |

Lecture 59Play Video |
Classical Runge-Kutta, ODE4Instructor: Cleve Moler ODE4 implements the classic Runge-Kutta method, the most widely used numerical method for ODEs over the past 100 years. Its major shortcoming is the lack of an error estimate. A simple model of the growth of a flame is an example that is used. |

Lecture 60Play Video |
Order, Naming ConventionsInstructor: Cleve Moler The digits in the name of a MATLAB ODE solver reflect its order and resulting accuracy. A method is said to have order p if cutting the step size in half reduces the error in one step by a factor of two to the power p+1. |

Lecture 61Play Video |
Estimating Error, ODE23Instructor: Cleve Moler ODE23 compares 2nd and 3rd order methods to automatically choose the step size and maintain accuracy. It is the simplest MATLAB solver that has automatic error estimate and continuous interpolant. ODE23 is suitable for coarse accuracy requirements. |

Lecture 62Play Video |
ODE45Instructor: Cleve Moler ODE45 is usually the function of choice among the ODE solvers. It compares 4th and 5th order methods to estimate error and determine step size. |

Lecture 63Play Video |
Stiffness, ODE23s, ODE15sInstructor: Cleve Moler A problem is said to be stiff if the solution being sought varies slowly, but there are nearby solutions that vary rapidly, so the numerical method must take small steps to obtain satisfactory results. The flame model demonstrates stiffness. |

Lecture 64Play Video |
Systems of EquationsInstructor: Cleve Moler An ODE involving higher order derivatives is rewritten as a vector system involving only first order derivatives. The classic Van der Pol nonlinear oscillator is provided as an example. The VdP equation becomes stiff as the parameter is increased. |

Lecture 65Play Video |
The MATLAB ODE SuiteInstructor: Cleve Moler The MATLAB documentation provides two charts summarizing the features of each of the seven functions in the MATLAB ODE suite. |

Lecture 66Play Video |
Tumbling BoxInstructor: Cleve Moler Throw a rectangular box with sides of three different lengths into the air. You can get the box to tumble stably about its longest axis or its shortest axis. But if you try to make it tumble about it middle axis, you will find the motion is unstable. |

Lecture 67Play Video |
Predator-Prey EquationsInstructor: Cleve Moler The classic Lotka-Volterra model of predator-prey competition is a nonlinear system of two equations, where one species grows exponentially and the other decays exponentially in the absence of the other. The program ""predprey"" studies this model. |

Lecture 68Play Video |
Lorenz Attractor and ChaosInstructor: Cleve Moler The Lorenz chaotic attractor was discovered by Edward Lorenz in 1963 when he was investigating a simplified model of atmospheric convection. It is a nonlinear system of three differential equations. The program ""lorenzgui"" studies this model. |