
Revisiting Fourier series for a function on [0, d], extended periodically, the lecture derives coefficients c_k from the integral of f(t) e^{-i ω_k t} over a period.
Follow how discrete Fourier coefficients become a continuous Fourier transform as the interval expands, and apply the Fourier transform and its inverse to analyze functions and solve partial differential equations.
Learn how to express frequency and wave number in Fourier transforms, switch between time and space domains, and generalize to multi-variable and four-vector forms with forward and inverse transforms.
Review the gradient as a three-dimensional vector of partial derivatives in x, y, z, and the Laplacian as the divergence of the gradient, a core PDE operator.
Solve partial differential equations with Fourier transforms in electrostatic settings, linking electric field divergence to charge density and the Laplace equation for the potential under far-field boundary conditions.
Apply the three-dimensional fourier transform to del^2 phi = F(x) with phi and its derivatives vanishing at infinity. Show that -k^2 phi_hat = F_hat and outline inverse transform for phi.
Derive the electric field of a point charge at the origin from a delta function charge density, compute the potential via an integral, and use E = -grad V.
Explore the diffusion problem by modeling a particle distribution F(x,t) in three-dimensional space and deriving the governing partial differential equation with boundary conditions.
The lecture derives the diffusion equation df/dt = D ∇^2 f by a Taylor expansion in independent x, y, z directions and second-order terms.
Apply Fourier transforms to the diffusion equation in time and space, derive the transformed equation, and handle the initial Dirac delta and boundary behavior with complex calculus.
Solve the diffusion equation using Fourier transforms, converting x to k and omega, deriving F double tilde, and outlining inverse transforms to time and space domains.
Compute the inverse transform of f tilde of k for the diffusion equation using complex calculus and residues, evaluating the contour integral and obtaining e^{-k^2 d}.
Derive the diffusion equation solution as a gaussian distribution with time-dependent variance by completing the square and then apply convolution with a delta initial condition for general initial functions.
Learn to solve a damped second-order linear ODE with a forcing term using the Fourier transform, yielding a time-domain solution from initial conditions plus a forced response from f(t).
Apply the method of characteristics to solve u_t = x u_x - u with u(x,0)=f(x) and |x|<1; get u(x,t)=e^{-t} f(x e^{t}), which vanishes as t grows.
Solve the two-dimensional Laplace equation in Cartesian coordinates on a rectangle by separation of variables. Build a cosine-series solution in x with y-dependent exponentials, applying Neumann and step-function boundary conditions.
Solves the Laplace equation outside a circle with polar coordinates using separation of variables, yielding a solution comprising a constant and a cos(3θ)/r^3 term that matches the boundary cos(3θ)+log 2.
Solve Laplace's equation in a semicircular region using polar coordinates, with u(a, θ)=g(θ) on the circle and an insulated diameter; derive a Fourier cosine series for u(r, θ).
Solve Laplace’s equation in an annulus using polar coordinates, enforce boundary data at a and B, and determine coefficients via orthogonality and linear systems.
Examine streamlines as particle trajectories in an incompressible, divergence-free velocity field, where velocity is tangential to lines of constant U. A Laplace equation solution yields circular streamlines.
Solve the nonhomogeneous heat equation by superposing a homogeneous solution with a forcing term, using Fourier series, separation of variables, and integrating factors for zero boundary and sine initial data.
Transform a nonhomogeneous heat equation with nonhomogeneous boundary and initial conditions into a homogeneous problem using a tilde function, then solve with W and V decomposition.
Split the nonhomogeneous heat equation into a homogeneous part and a nonhomogeneous part, solve each (via separation of variables as needed), and sum to get U = W + V.
Apply the D'Alembert formula to a nonhomogeneous wave equation by splitting into two problems - a homogeneous part and a forcing part - and summing their solutions.
Apply the D'Alambert formula to solve the homogeneous wave equation with given boundary and initial conditions, using the element representation and recalling the required sine terms.
Prove the energy conservation law for the homogeneous wave equation with normal boundary conditions, showing that the energy E(t) = 1/2 ∫0^l [u_t^2 + c^2 u_x^2] dx remains constant.
Solving Partial Differential Equations using the Fourier Transform: A Step-by-Step Guide
Course Description:
This course is designed to provide a comprehensive understanding of how the Fourier Transform can be used as a powerful tool to solve Partial Differential Equations (PDE). The course is divided into three parts, each building on the previous one, and includes bonus sections on the mathematical derivation of the Heisenberg Uncertainty Principle.
Part 1: In this part, we will start with the basics of the Fourier series and derive the Fourier Transform and its inverse. We will then apply these concepts to solve PDE's using the Fourier Transform. Prerequisites for this section are Calculus and Multivariable Calculus, with a focus on topics related to derivatives, integrals, gradient, Laplacian, and spherical coordinates.
Part 2: This section introduces the heat equation and the Laplace equation in Cartesian and polar coordinates. We will solve exercises with different boundary conditions using the Separation of Variables method. This section is self-contained and independent of the first one, but prior knowledge of ODEs is recommended.
Part 3: This section is dedicated to the Diffusion/Heat equation, where we will derive the equation from physics principles and solve it rigorously. Bonus sections are included on the mathematical derivation of the Heisenberg Uncertainty Principle.
Course Benefits:
Gain a thorough understanding of the Fourier Transform and its application to solving PDE's.
Learn how to apply Separation of Variables method to solve exercises with different boundary conditions.
Gain insight into the Diffusion/Heat equation and how it can be solved.
Bonus sections on the Heisenberg Uncertainty Principle provide a deeper understanding of the mathematical principles behind quantum mechanics.
Prerequisites:
Calculus and Multivariable Calculus with a focus on derivatives, integrals, gradient, Laplacian, and spherical coordinates.
Prior knowledge of ODEs is recommended.
Some knowledge of Complex Calculus and residues may be useful.
Who is this course for?
Students and professionals with a background in Mathematics or Physics looking to gain a deeper understanding of solving PDE's using the Fourier Transform.
Those interested in the mathematical principles behind quantum mechanics and the Heisenberg Uncertainty Principle.