Traditionally the engineering modeling process is based on first principles, which usually yields large, complex and detailed models of a dynamic system. As an alternative, classical system identification procedures often produce simple (linear) models ignoring additional domain knowledge. In this context, numerical models, e.g. multi-body models or finite-element simulations based on first-principles are used to predict the system behavior. If only a small number of simulation outputs are needed, massive computational power is wasted on computing grid data which is of no further interest. In this paper a different approach using engineering techniques, such as dimensional analysis, coupled with knowledge discovery methods, such as e.g. neural networks or k-nearest neighbor search, is used to predict the dynamic system behavior from only a few characteristic input parameters and the given initial or boundary conditions. The dynamic system is therefore modeled as a nonlinear static mapping whose parameters are estimated from experiment as well as from simulation. This static mapping allows for very fast prediction times compared to computationally intense numerical simulations. Additionally, some of the mapping methods allow the calculation of sensitivities, which in turn allow e.g. the ranking of the inputs according to their contribution to the output parameters. The presented approach to dynamic system analysis is first described in detail, then some of the used methods are described and the usefulness of the approach is demonstrated in the example of a non-linear spring-mass-damper system.