Analysis of car crash simulation data with nonlinear machine learning methods
Nowadays, product development in the car industry heavily relies on numerical simulations. For example, it is used to explore the influence of design parameters on the weight, costs or functional properties of new car models. Car engineers spend a considerable amount of their time analyzing these influences by inspecting the arising simulations one at a time. Here, we propose using methods from machine learning to semi-automatically analyze the arising finite element data and thereby significantly assist in the overall engineering process. We combine clustering and nonlinear dimensionality reduction to show that the method is able to automatically detect parameter dependent structure instabilities or reveal bifurcations in the time-dependent behavior of beams. In particular we study recent nonlinear and sparse grid approaches, respectively. Our examples demonstrate the strong potential of our approach for reducing the data analysis effort in the engineering process, and emphasize the need for nonlinear methods for such tasks.