Parametric optimisation study

[RemDelaporteMathurin]

Instead of a "deterministic" simulation, users should be able to set parameters as free parameters and run parametric optimisation studies based on a set of constraints and goals.

Examples:

• In the ARC fuel cycle simulation, minimise the startup storage inventory while ensuring the total energy produced in the first year is greater than X.
• Find the mass transfer coefficients that fit the tritium release curve that was experimentally measured (see this tutorial)

UI needs:

• Set a parameter as free parameter: similar to Parametric studies #181 users would be able to set a parameter as "free"
• Give bounds to a parameter (eg. parameter A cannot be negative)
• Define a cost function/error python function def error(p): ... return e
• Select an optimiser (from scipy.optimise methods?)

[kwmcbride]

I've decided to work on this feature. I've worked with quite a bit of optimization before so this is fairly straightforward.

I've a version of the tritium model you mentioned built in pathview that I exported to a Jupyter notebook. I didn't use the exact areas on the linked site and generated some fake data in the ballpark. The results will be off, but it does not change the general nature of the parameter fitting. I built this example using scipy.optimize.least_squares.

The next step is making the code formal and conform to the pathsim style, followed by getting it to work with the UI in pathview.