During my last week in India, I contributed to the MOOSE project by providing an interface to a parameter fitting tool. Now parameters of MOOSE models can be searched by doing some tiny Python scripting.
When you have experimental data on a phenomenon, and you intend to create a computational model of it, usually you need to apply parameter fitting/searching techniques. These methods help you determine those parameters of your model that you have no reference on. Parameter fitting using MOOSE models are accomplished through utilizing Optimizer [1], a parameter fitting tool developed for neural simulations, which I came across by pure chance.
Results 'layer' from the Optimizer tutorial. Optimizer has a simple GUI to guide the user to a successive parameter fitting.
