impulse homepage
homepage projects methods/ models lectures publications about us links
Methods & Models Methods
methods
models
A mathematical models is the heart of any decision support system; the problem every mathematical modeler has to face: The more realistic a model, the more complex its behaviour - much to the users' distress. In contrast, scientist only trust models that closely reproduce reality. IMPULSE wants to resolve this conflict by using innovative methods:

» model aggregation
» data assimilation

Model aggregation - how to say more with less

It hardly makes sense to simplify a complex model by leaving out details that appear to be unimportant: Relevant mechanisms or co-effects might be overlooked. We must rather rethink our traditional approaches: How can we say more with less?

Especially two concepts form the basis of model aggregation:
» By introducing effective parameters models with complex structures (e.g. a large ecosystem model) are reduced to simpler models, that still fit the specified phenomenon.
» Models with complicated boundary conditions can use simple boundary condition without loss of precision, if coupled with an appropriate nonlinear data model, e.g. neural networks. In this way the resolution of a hydrodynamic model of the North Sea can be reduced from 500m to 20km.

Data assimilation - how to make a more reliable forecast

The forecast of a model is anything but precise: Too many inaccuracies can distort the forecast. Therefore, it is important to continuously verify the result of a model: If required model parameters and state variables need to be changed, in order to improve the forecast. This can be acchived by comparing existing data with model results.

Data assimilation not only improves model forecasts but also helps us to learn about the major weaknesses of a model.