Callback for forward solution at iterate N, timestep K?
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Martin Sandve Alnæs
I'd like to investigate how the forward solution converges (or not) toward the optimal state, by e.g. storing a selection of snapshots at all iterates x a subset of the timesteps, or storing/printing some norms and functionals computed from the intermediate forward solutions at a subset of the timesteps. Would it be possible to register a callback from which I can get access to these intermediate function values for such customized "postprocessing"? This callback would be called at the end of each timestep of each replay in an optimization run, with functions available by name through a dict or some interface.
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