[petsc-users] Adaptive controllers in TS

[Miguel Angel Salazar de Troya]

I decided to implement the continuous adjoint because it is clearer to me
how to do it and the gradient will converge anyways. I was trying to
interpolate the solution, but in the adjoint analysis I need to do it once
the simulation has finished. I have saved all the solutions for each time
step. My idea was to reset the TS at the time step immediately previous to
the time step in which I want to interpolate to:

interpolate_timestep : where I want to interpolate to
previous_timestep : last time step in our time step history which is
smaller that the interpolate_timestep
Current_Sol : Solution at the time step previous_timestep

TSSetTime(ts,previous_timestep);
TSSetSolution(ts,Current_Sol);
TSSetRetainStages(ts,PETSC_TRUE);

TSInterpolate(ts,interpolate_timestep,X);

Vector X should have a close value to Current_Sol, but it's nowhere close.
I also compare it to the solution of the next time step after
previous_timestep (therefore interpolate_timestep is between these guys)
and it's not close either. I've read that TSInterpolate() has to be
extended to support continuous adjoints.

Thanks
Miguel

On Tue, Oct 21, 2014 at 6:04 PM, Miguel Angel Salazar de Troya <
salazardetroya at gmail.com> wrote:

> That might be a reasonable argument, but I'm not sure. This is one of the
> papers that explains it. I'm re-reading to see if I skipped any details:
>
> http://www.sciencedirect.com/science/article/pii/S0377042709006062
>
> Miguel
>
> On Mon, Oct 20, 2014 at 11:42 PM, Jed Brown <jed at jedbrown.org> wrote:
>
>> Miguel Angel Salazar de Troya <salazardetroya at gmail.com> writes:
>>
>> > Thanks for your response
>> >
>> > I'm struggling with this problem because the literature is not clear
>> for me
>> > on how to calculate the discrete adjoint with adaptive time stepping
>> > algorithms. They cover the details when automatic differentiation tools
>> are
>> > used. They mention that because the time step depend on the solution, it
>> > also depends on the parameter. Hence, there are terms that represent the
>> > derivative of the time step w.r.t. the parameters. What it is confusing
>> is
>> > that they mention these terms must be removed. I don't understand this.
>> I'm
>> > planning to hard-code the discrete adjoint problem (and use the TS if
>> > possible).
>>
>> Are they suggesting that the time step sizes for a given run should be
>> frozen (at least locally) so that you have consistent gradients for a
>> while?
>>
>
>
>
> --
> *Miguel Angel Salazar de Troya*
> Graduate Research Assistant
> Department of Mechanical Science and Engineering
> University of Illinois at Urbana-Champaign
> (217) 550-2360
> salaza11 at illinois.edu
>
>


-- 
*Miguel Angel Salazar de Troya*
Graduate Research Assistant
Department of Mechanical Science and Engineering
University of Illinois at Urbana-Champaign
(217) 550-2360
salaza11 at illinois.edu
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