Sorry to be pushy, but could anyone help me on this? Thanks Miguel
On Thu, Oct 23, 2014 at 10:46 AM, Miguel Angel Salazar de Troya < salazardetr...@gmail.com> wrote: > 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 < > salazardetr...@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 <j...@jedbrown.org> wrote: >> >>> Miguel Angel Salazar de Troya <salazardetr...@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 >> salaz...@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 > salaz...@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 salaz...@illinois.edu
