The navROM application calculates a Reduced Order Model using one of the methods POD or Isomap based on a set of parameterized input data (“snapshots”).

A ROM is the model of a complex process that has been simplified by applying mathematical methods in such a way that it is (much) easier to handle. A good ROM is characterized by the fact that the relevant properties of the simplified model match those of the complex process very well. To train the models, we need data sets, for example from CFD, computed for different parameter combinations. We then perform the model reduction using the POD or Isomap methods. The former uses a singular value decomposition, the latter identifies low-dimensional structures in high-dimensional data sets. In everyday industrial applications, a ROM is often practical: the reduced model can be used to compute predictions for new parameter combinations very quickly – spatially and for all variables. So instead of having to compute a new solution using numerical simulation, which can take hours or days, the solution is estimated within seconds by evaluating a ROM.