This might be a naive question, but when applying a implicit discretization to a PDE with a source term, should the source be averaged in time? For example if we take the diffusion equation with a non-linear source term,

$$ u_t = u_{xx}+s(x,t,u) $$

We can apply the following central difference implicit scheme to the differential term,

$$ \frac{u_j^{n+1} - u_j^n}{\Delta t} = \left[ (1-\theta) (u_{j-1} - 2u_{j} + u_{j+1}) + \theta (u_{j-1} - 2u_{j} + u_{j+1})\right] + s(x,t,u) $$

but how should $s(x,t,u)$ be treated? Should we simply take the value the $n$ time point (this is what I have always done in the past),

$$ s(x,t) = s_j^n $$

or averaged over time,

$$ s(x,t) = (1-\theta)s_j^{n+1} + \theta s_j^{n} $$

I am not sure it is possible to implement a time average in this way because, in general, the $n+1$ points in time are unknowns!

Is this a silly question? Or is there some way of improving the time integration of the above equation by taking averages in time?


2 Answers 2


If the right hand side were independent of $u$ then one would generally use the averaged form $$ (1-\theta)s_(x,t^{n+1}) + \theta s(x,t^n). $$ In the nonlinear case you can't do that easily, as you note, but you can at least use some kind of extrapolation, for example approximate $$ (1-\theta)s(x,t^{n+1},u^{n+1}) + \theta s(x,t^n,u^n) \approx (1-\theta)s\left(x,t^{n+1},u^{n}+\Delta t\frac{u^n-u^{n-1}}{\Delta t}\right) + \theta s(x,t^n,u^n) . $$ You will find more tricks like this in the ODE literature -- read up on semi-implicit methods for ODEs of the kind $\dot x(t) = A x(t) + f(x(t),t)$.

  • $\begingroup$ I second that. Here's a classic paper describing IMEX methods:epubs.siam.org/doi/abs/10.1137/0732037 $\endgroup$
    – mmirzadeh
    Commented Jun 1, 2013 at 5:55
  • $\begingroup$ Thanks, will grab that next time I'm on campus. Knowing the names for the various techniques helps. When I started this equation I didn't know I was asking an IMEX equation. $\endgroup$
    – boyfarrell
    Commented Jun 2, 2013 at 11:03

The most robust way is solving it implicitly, otherwise for stiff nonlinearity in the source function $s(u)$ you will have to use very small time step to maintain numerical stability.


$u_t = s(t,u)$


$u^{n+1} = u^n + dt \; s(t+dt, u^{n+1})$

Here one can use average between n and n+1 in the RHS to increase the order, or use more temporal points for even higher order in time. But to make it simple let's assume first order implicit time-stepping.

Now the equation can be cast in the form


which can be solved by the Newton method, and for the discretized system doing linear solves with the Krylov subspace is the best known approach, so this is the Newton-Krylov method.


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