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Reduced order modelling relies on representing complex dynamical systemsusing simplified modes, which can be achieved through Koopman operatoranalysis. However, computing Koopman eigen pairs for high-dimensionalobservable data can be inefficient. This paper proposes using deepautoencoders, a type of deep learning technique, to perform non-lineargeometric transformations on raw data before computing Koopman eigen vectors.The encoded data produced by the deep autoencoder is diffeomorphic to amanifold of the dynamical system, and has a significantly lower dimension thanthe raw data. To handle high-dimensional time series data, Takens's time delayembedding is presented as a pre-processing technique. The paper concludes bypresenting examples of these techniques in action.

Autoencoding for the 'Good Dictionary' of eigen pairs of the Koopman Operator