Open benRenard opened 1 month ago
smash.optimize_control_info
and smash.bayesian_optimize_control_info
methods. Then standardize these arguments, apply wrap_parameters_to_control, set new control values (setattr(model._parameters.control, "x", x)), and finally run the forward model with the updated control vector.
For many applications, one needs to pass a function that evaluates the cost (or the log-posterior) from the control vector. For instance:
To facilitate this, the following functions would be useful (interfaces are approximate, just to convey the idea):