Numerically solving a non-local PDE

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Discussion Overview

The discussion revolves around numerically solving a non-local partial differential equation (PDE) defined on the region ##x \in [0,1]## and ##t \in (0, \infty)##. The equation involves an integral term that complicates the numerical solution, prompting participants to explore various discretization and numerical integration techniques.

Discussion Character

  • Technical explanation
  • Mathematical reasoning
  • Exploratory

Main Points Raised

  • One participant presents a PDE whose solution depends on the values of the function at all points in space, highlighting the challenge posed by the integral term.
  • Another participant questions the exclusion of the term where ##j = i## in the sum and asks about the boundary conditions necessary for the numerical solution.
  • A suggestion is made to express the discretized form of the PDE as a matrix equation, indicating that the matrix ##M## will depend on the numerical integration method used.
  • Another approach is proposed using Gauss-Legendre quadrature for approximating the integral, which is noted to be exact for polynomials of a certain order.

Areas of Agreement / Disagreement

Participants present different methods for numerical integration and discretization, indicating that multiple approaches are being considered. There is no consensus on a single method or solution at this point.

Contextual Notes

The discussion includes various assumptions regarding boundary conditions and the choice of numerical integration techniques, which may affect the accuracy and applicability of the proposed methods.

ergospherical
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I have a PDE to solve numerically on the region ##x \in [0,1]## and ##t \in (0, \infty)##. It is of the form:$$\frac{\partial f(x,t)}{\partial t} = g(x,t) + \int_0^1 h(x, x') f(x', t) dx'$$The second term is the tricky part. The change in ##f(x,t)## at ##x## depends on the value ##f(x',t)## of every other point ##x'## in the space.

In discretized form, it is something like$$f(x_i,t_{j+1}) = f(x_i,t_j) + \Delta t \left[ g(x_i,t_i) + \sum_{j \neq i} h(x_i, x_j) f(x_j, t_i) \right]$$How can I apply an accurate scheme like Runge-Kutta to this system?
 
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Why do you exclude j = i from the sum? And what are the boundary conditions?

After discretizing in space, you must end up with <br /> \dot{\mathbf{f}} = M\mathbf{f} + \mathbf{g} where f_i(t)= f(x_i, t) etc. with the matrix M depending on how you do the numerical integration. For the trapezoid rule with x_n = \frac{n}{N - 1} = n \Delta x, <br /> \int_0^1 h(x,x&#039;)f(x&#039;,t)\,dx&#039; \approx \frac12 \Delta x (h(x,x_0)f_0 + 2h(x,x_1)f_1 + \dots + h(x,x_{N-1})f_{N-1}) so that <br /> M_{ij} = \begin{cases} \frac12 \Delta x h(x_i,x_j) &amp; j = 0, N - 1 \\<br /> \Delta x h(x_i, x_j) &amp; j = 1 , \dots, N - 2 \end{cases} You may need to adjust rows 0 and N - 1 in order to enforce a boundary condition.
 
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Another alternative is to use Gauss-Legendre quadrature, <br /> \int_0^1 h(x,x&#039;)f(x&#039;,t)\,dx = \frac12\int_{-1}^1 h(x,\tfrac12(z + 1))f(\tfrac12(z + 1),t)\,dz<br /> \approx \sum_{j=0}^{N-1} \tfrac12 w_j h(x,\tfrac12(z_j + 1))f_j which is exact for polynomials of order up to 2N - 1 in x&#039;. The z_i \in [-1,1] are then the Gauss-Legendre points with x_i = \frac12(z_i + 1) and <br /> M_{ij} = \frac12 w_jh(x_i,x_j).
 
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