Reduced Order Modelling using Dynamic Mode Decomposition and Koopman Spectral Analysis with Deep Learning
In the industry simulation models are commonly used in system development. These models can become complicated in order to capture the physical behaviour of the underlying dynamical system. A high-fidelity representation, which can result in long simulation times, is in some settings not strictly required. A method for overall model fidelity reduction is therefore of interest. In this thesis, two