Increasingly, model prototyping rely on computational models to understand complex phenomena having multiple, interacting components whose aggregate behavior can be understood only by the simultaneous analysis of their individual behaviors.

model prototyping article

In approaching such complexity, scientists have naturally adopted a "divide-and-conquer" approach, first creating models of isolated sub processes. Many of these models are now well understood and captured in robust programs; they constitute the important first steps towards the ultimate goal of understanding complex interactions among physical or biological processes.

The challenge now is for the ridge community as well as many other communities is to couple their isolated models into self-consistent representations of more complex processes. Coupling is more complex than the mere composition of computational elements. Scientists are faced with the poorly understood task of establishing sophisticated time-varying relationships between models and large, multi-dimensional data that are heterogeneous in quantity, quality, scale, type, and ultimately importance. To accomplish this, they will need more than standard coupling mechanisms; they will need support for dynamically exploring model correlations and relationships at a very high, domain- specific model prototyping level.