Deep Learning the Dynamics of Mechanical Systems

Abstract

This paper examines whether complex high-dimensional data that describes the dynamics of a cantilever beam can be transformed into a less complex system. In particular, the transformation that is examined is the reduction of the dimension. An essential aspect of this study involves the implementation of a linear autoencoder, which is a type of machine learning model that possesses the capability to effectively reduce the dimensionality of input data while adeptly reconstructing the original dataset. The model performs well and is successful in reconstructing complex data via the less complex system. However, the model struggles if the dynamics are made more complex by adding an external force. Although the dynamics seem to be present in the results, the amplitudes differ.