and t should be declared as Constant which can be updated using t.assign(<value>) to avoid recompilation.

---

From: firedrake-bounces@imperial.ac.uk <firedrake-bounces@imperial.ac.uk> on behalf of Sagiyama, Koki <k.sagiyama@imperial.ac.uk>
Sent: 21 January 2021 11:20
To: Artur Palha <artur.palha@gmail.com>; firedrake <firedrake@imperial.ac.uk>
Subject: Re: [firedrake] Fast assembly of time-dependent bilinear forms

 

Dear Artur,

Adding two matrices I think is not supported as, in general, those matrices can have different sparsity patterns, and adding two matrices of different sparsity patterns costs.

As for your problem, I would simply assemble the entire a1 + a2 at every timestep.

If:

a1 = u*v*dx

a2 = u*g(t)*v*dx

where g(t) is the time dependent function, we have:

a1+a2=u*(1+g(t))*v*dx

so the cost of adding a1 to a2 is very small (just adding 1).

To indicate that your matrix is not constant in time, you must set `constant_jacobian=False` when constructing the LinearVariationalProblem:

https://www.firedrakeproject.org/firedrake.html#firedrake.variational_solver.LinearVariationalProblem

Thanks,

Koki

---

From: firedrake-bounces@imperial.ac.uk <firedrake-bounces@imperial.ac.uk> on behalf of Artur Palha <artur.palha@gmail.com>
Sent: Wednesday, January 20, 2021 4:17 PM
To: firedrake <firedrake@imperial.ac.uk>
Subject: [firedrake] Fast assembly of time-dependent bilinear forms

 

Hello Everyone,

I have an issue I have been struggling with for some time and I am unable to find a solution for it and I hope someone can help me with this.

The issue is that I have a time dependent problem that results in the following weak form (already discretised in time):

Find u^{n+1} \in V such that

a_1(u^{n+1}, nu) + a_2(u^{n+1}, nu; w^{n+1/2)) = b(u^{n}),    for all nu \in V

- a_1 is a time-constant bilinear form, meaning that I can assemble it once for all time steps

- a_2 is a time-dependent billinear form since it depends on w and this changes with the time step.

My first approach was to assemble a_1 (using trial and test functions) in order to obtain a matrix. I could do this once, before the time stepping loop. Then, inside the time loop, I assembled a_2. As I tried to add the two resulting matrices I got an error stating addition is not possible for matrices.

I then looked into LinearVariationalProblem and LinearVariationalSolver. My problem with this is that I am unable to understand if by defining the LinearVariational as the sum of a_1 and a_2, both will be kept constant or if only the a_2 part will be updated at each time step.

So my question is: is there a standard way of solving this problem? Or do I need to recompute the whole system matrix for every time step (which is what I am doing now)?

Thank you for your help.

Kind regards,

-artur palha

_______________________________________________
firedrake mailing list
firedrake@imperial.ac.uk
https://mailman.ic.ac.uk/mailman/listinfo/firedrake