feynman1 said:
Define f[v(x),v'(x),...] as the residual of the original PDE
You do not have the residual of the original PDE. What you do have is the residual of the discretized PDE.
Now, if you use iterative solvers like Krylov subspace methods (for instance GMRES
https://en.wikipedia.org/wiki/Generalized_minimal_residual_method) then the Euclidian norm of the residual at iteration
i can be used to monitor the convergence towards the numerical solution in this discretized subspace. These residuals are reported by numerical PDE solver software like Comsol or OpenFOAM.
However, if your residual is zero, it only means that you have found the solution of the
discretized PDE to within machine precision. You still do not know how large the error is between your discretized solution and the solution of the original PDE. For this you then need to do a mesh refinement study. For example you can take your exact solution of the discretized problem on mesh
N and project it onto a larger/finer mesh
2N and recompute the residual. The residual will now not be zero anymore and you have to re-iterate until convergence. The hope is that if you make your mesh larger and larger, these residual changes on the new mesh will become smaller and your discrete solution will converge to the continuous solution.
feynman1 said:
then why not a good estimate of the error?
In practice, people look at residuals of iterative methods and associate this with 'the error'. There are relationships (from functional analysis) between the norm of the residual and the discretization error, and the actual continuous solution of the original PDE. They depend on the PDE (+boundary conditions) and the numerical method used and they give you information about the order of the convergence (next to the proof that the numerical method actually converges).
You can find this kind of detailed information for instance in the book of Babuska and Strouboulis 'The Finite Element Method and its Reliability' .