Re: [petsc-users] Adaptive controllers in TS
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
Re: [petsc-users] Adaptive controllers in TS
Miguel Angel Salazar de Troya salazardetr...@gmail.com writes: Sorry to be pushy, but could anyone help me on this? Your last email didn't have a specific question, so I'm not sure how to answer. What you want to do is possible, but not trivial and might require library modifications to avoid doing something perverse. Hong might be able to comment on his status or even somewhere that you could help. 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); Presumably this is a fragment and you did TSStep in here, otherwise how do you expect valid stage values? 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. signature.asc Description: PGP signature
Re: [petsc-users] Adaptive controllers in TS
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
Re: [petsc-users] Adaptive controllers in TS
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
Re: [petsc-users] Adaptive controllers in TS
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? pgpefm9_zJEkF.pgp Description: PGP signature
[petsc-users] Adaptive controllers in TS
Hi all I'm trying to find out what is the adaptive controller scheme used in the Runge-Kutta time stepping algorithm. Basically I want to know the function that determines the next time step. I see that the next time step is set with the function TSAdaptChoose(), which calls the pointer function (*choose)() within the structure _TSAdaptOps, but where is this set? I need to know this for an adjoint analysis to calculate gradients. I was wondering if there is an example of such analysis with the TS structure. I've seen that the RK source file includes a funciton to interpolate the solution, something that is necessary in an adjoint analysis, hence my question about the example. Thanks Miguel -- *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
Re: [petsc-users] Adaptive controllers in TS
Miguel Angel Salazar de Troya salazardetr...@gmail.com writes: Hi all I'm trying to find out what is the adaptive controller scheme used in the Runge-Kutta time stepping algorithm. Basically I want to know the function that determines the next time step. I see that the next time step is set with the function TSAdaptChoose(), which calls the pointer function (*choose)() within the structure _TSAdaptOps, but where is this set? via TSAdaptSetType. The default will be TSAdaptChoose_Basic (adaptbasic.c). You can find this information easily by running in a debugger. I need to know this for an adjoint analysis to calculate gradients. I was wondering if there is an example of such analysis with the TS structure. I've seen that the RK source file includes a funciton to interpolate the solution, something that is necessary in an adjoint analysis, hence my question about the example. Interpolation arises for continuous adjoints. Be aware that if you want consistent gradients, you'll want a discrete adjoint, which integrates backward in time using the adjoint RK method. The stages of the adjoint RK method coincide with the forward method, so no interpolation is needed. Hong (Cc'd) is developing an example that can integrate discrete adjoint equations and we intend to eventually incorporate that functionality into the library. pgp9KxFGrhL_8.pgp Description: PGP signature
Re: [petsc-users] Adaptive controllers in TS
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). I know this might not be the place to ask this. Maybe Hong can help me here. Miguel On Sun, Oct 19, 2014 at 1:41 PM, Jed Brown j...@jedbrown.org wrote: Miguel Angel Salazar de Troya salazardetr...@gmail.com writes: Hi all I'm trying to find out what is the adaptive controller scheme used in the Runge-Kutta time stepping algorithm. Basically I want to know the function that determines the next time step. I see that the next time step is set with the function TSAdaptChoose(), which calls the pointer function (*choose)() within the structure _TSAdaptOps, but where is this set? via TSAdaptSetType. The default will be TSAdaptChoose_Basic (adaptbasic.c). You can find this information easily by running in a debugger. I need to know this for an adjoint analysis to calculate gradients. I was wondering if there is an example of such analysis with the TS structure. I've seen that the RK source file includes a funciton to interpolate the solution, something that is necessary in an adjoint analysis, hence my question about the example. Interpolation arises for continuous adjoints. Be aware that if you want consistent gradients, you'll want a discrete adjoint, which integrates backward in time using the adjoint RK method. The stages of the adjoint RK method coincide with the forward method, so no interpolation is needed. Hong (Cc'd) is developing an example that can integrate discrete adjoint equations and we intend to eventually incorporate that functionality into the library. -- *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
