Example: approximating derivatives using DG

MeshData can be used to compute DG derivatives. Suppose $f$ is a differentiable function and the domain $\Omega$ can be decomposed into non-overlapping elements $D^k$. The approximation of $\frac{\partial f}{\partial x}$ can be approximated using the following formulation: find piecewise polynomial $u$ such that for all piecewise polynomials $v$

\[\int_{\Omega} u v = \sum_k \left( \int_{D^k} \frac{\partial u}{\partial x}v + \int_{\partial D^k} \frac{1}{2} \left[u\right]n_x v \right)\]

Here, $\left[u\right] = u^+ - u$ denotes the jump across an element interface, and $n_x$ is the $x$-component of the outward unit normal on $D^k$.

Discretizing the left-hand side of this formulation yields a mass matrix. Inverting this mass matrix to the right hand side yields the DG derivative. We show how to compute it for a uniform triangular mesh using MeshData and StartUpDG.jl.

We first construct the triangular mesh and initialize md::MeshData.

using StartUpDG
using Plots

N = 3
K1D = 8
rd = RefElemData(Tri(),N)
VX,VY,EToV = uniform_mesh(Tri(),K1D)
md = MeshData(VX,VY,EToV,rd)

We can approximate a function $f(x,y)$ using interpolation

f(x,y) = exp(-5*(x^2+y^2))*sin(1+pi*x)*sin(2+pi*y)
@unpack x,y = md
u = @. f(x,y)

or using quadrature-based projection

@unpack Pq = rd
@unpack x,y,xq,yq = md
u = Pq*f.(xq,yq)

We can use scatter in Plots.jl to quickly visualize the approximation. This is not intended to create a high quality image (see other libraries, e.g., Makie.jl,VTK.jl, or Triplot.jl for publication-quality images).

@unpack Vp = rd
xp,yp,up = Vp*x,Vp*y,Vp*u # interp to plotting points
scatter(xp,yp,uxp,zcolor=uxp,msw=0,leg=false,ratio=1,cam=(0,90))

Both interpolation and projection create a matrix u of size $N_p \times K$ which contains coefficients (nodal values) of the DG polynomial approximation to $f(x,y)$. We can approximate the derivative of $f(x,y)$ using the DG derivative formulation

function dg_deriv_x(u,rd::RefElemData,md::MeshData)
  @unpack Vf,Dr,Ds,LIFT = rd
  @unpack rxJ,sxJ,J,nxJ,mapP = md
  uf = Vf*u
  ujump = uf[mapP]-uf

  # derivatives using chain rule + lifted flux terms
  ux = rxJ.*(Dr*u) + sxJ.*(Ds*u)  
  dudxJ = ux + LIFT*(.5*ujump.*nxJ)

  return dudxJ./J
end

We can visualize the result as follows:

dudx = dg_deriv_x(u,rd,md)
uxp = Vp*dudx
scatter(xp,yp,uxp,zcolor=uxp,msw=0,leg=false,ratio=1,cam=(0,90))

Plots of the polynomial approximation $u(x,y)$ and the DG approximation of $\frac{\partial u}{\partial x}$ are given below

dudx ⠀