Numerically solving a non-local PDE

In summary, "Numerically solving a non-local PDE" discusses methods for addressing partial differential equations (PDEs) that exhibit non-local behavior, where the solution at a point depends on values from a broader region rather than just its immediate vicinity. The paper outlines various numerical techniques, including discretization strategies and algorithms, to effectively approximate solutions to these complex equations. It emphasizes the challenges posed by non-local interactions and provides insights into the stability and convergence of the proposed methods. Overall, the work contributes to the understanding and application of numerical solutions for non-local PDEs in various scientific fields.
  • #1
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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  • #2
Why do you exclude [itex]j = i[/itex] from the sum? And what are the boundary conditions?

After discretizing in space, you must end up with [tex]
\dot{\mathbf{f}} = M\mathbf{f} + \mathbf{g}[/tex] where [itex]f_i(t)= f(x_i, t)[/itex] etc. with the matrix [itex]M[/itex] depending on how you do the numerical integration. For the trapezoid rule with [itex]x_n = \frac{n}{N - 1} = n \Delta x[/itex], [tex]
\int_0^1 h(x,x')f(x',t)\,dx' \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})[/tex] so that [tex]
M_{ij} = \begin{cases} \frac12 \Delta x h(x_i,x_j) & j = 0, N - 1 \\
\Delta x h(x_i, x_j) & j = 1 , \dots, N - 2 \end{cases}[/tex] You may need to adjust rows [itex]0[/itex] and [itex]N - 1[/itex] in order to enforce a boundary condition.
 
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  • #3
Thanks, I'm making progress...
 
  • #4
Another alternative is to use Gauss-Legendre quadrature, [tex]
\int_0^1 h(x,x')f(x',t)\,dx = \frac12\int_{-1}^1 h(x,\tfrac12(z + 1))f(\tfrac12(z + 1),t)\,dz
\approx \sum_{j=0}^{N-1} \tfrac12 w_j h(x,\tfrac12(z_j + 1))f_j[/tex] which is exact for polynomials of order up to [itex]2N - 1[/itex] in [itex]x'[/itex]. The [itex]z_i \in [-1,1][/itex] are then the Gauss-Legendre points with [itex]x_i = \frac12(z_i + 1)[/itex] and [tex]
M_{ij} = \frac12 w_jh(x_i,x_j).[/tex]
 
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