Master / PhD student / PostDoc group

Have combined approach with different parameter sets with a switch depending on the conditions But this is a first step and the final aim should be to improve the model to avoid this trick

Master / PhD student / PostDoc group We have a model => We want to reach a target We know the target => We should calibrate/choose/build the model to fulfill it

Master / PhD student / PostDoc group Work on the understanding the processes before spending a lot of energy maximize a criteria. First understand processes and then model it. In practice, it may be hard to implement the knowledge we have in the « model world » We should transfer the lessons we learnt during calibration rather than parameters values