In the process of geometry-based optimization, the problem of having to model a resulting geometry without knowing the exact values of characteristic parameters can arise. In order to open and further edit the optimized geometry inside the CAESES environment, the parameter set belonging to the new shape needs to be determined. The use of neural networks offers a great opportunity to solve this kind of problem since they are a powerful tool for constructing a predictive model based on data.