Error handling in invalid simulations

[Retiefasaurus]

I'd like to open a discussion possible techniques of handling errors when simulations results do not make sense.

For example:
a DEM simulation with a certain combination of parameters might become unstable and its output may be considered invalid (Nan, Inf etc.) what are some techniques in Grain Learning to handle this situation without restarting the whole calibration process.

Proposed solutions:

• We can resample parameters from the same distribution with an error handling callback function
• Let SMC identify invalid simulations and reject those solutions in the calculation of the posterior distribution