Print Email Facebook Twitter Deep Learning the Dynamics of Mechanical Systems Title Deep Learning the Dynamics of Mechanical Systems Author Wigmans, Bram (TU Delft Electrical Engineering, Mathematics and Computer Science) Contributor Heinlein, A. (mentor) Jain, S. (mentor) Degree granting institution Delft University of Technology Programme Applied Mathematics Date 2023-10-20 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. Subject Machine learningAutoencoderdynamics To reference this document use: http://resolver.tudelft.nl/uuid:6f7bcab1-096b-4290-b582-7e732010411d Part of collection Student theses Document type bachelor thesis Rights © 2023 Bram Wigmans Files PDF Deep_Learning_Mechanics_f ... nal_1_.pdf 12.72 MB Close viewer /islandora/object/uuid:6f7bcab1-096b-4290-b582-7e732010411d/datastream/OBJ/view