  For sequences of complex 3D shapes in time we present a general approach to
detect patterns for their analysis and to predict the deformation by making use
of structural components of the complex shape. We incorporate long short-term
memory (LSTM) layers into an autoencoder to create low dimensional
representations that allow the detection of patterns in the data and
additionally detect the temporal dynamics in the deformation behavior. This is
achieved with two decoders, one for reconstruction and one for prediction of
future time steps of the sequence. In a preprocessing step the components of
the studied object are converted to oriented bounding boxes which capture the
impact of plastic deformation and allow reducing the dimensionality of the data
describing the structure. The architecture is tested on the results of 196 car
crash simulations of a model with 133 different components, where material
properties are varied. In the latent representation we can detect patterns in
the plastic deformation for the different components. The predicted bounding
boxes give an estimate of the final simulation result and their quality is
improved in comparison to different baselines.
