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Understanding Bursting Dynamics in Two-dimensional Turbulence Using Deep Convolutional Autoencoders
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Understanding turbulence is a long-standing problem in the field of fluid dynamics. Here we attempt to further this understanding and classify bursting events in the setting of two-dimensional Kolmogorov flow. To do this we utilise deep convolutional autoencoders and symmetry properties of Kolmogorov flow to construct data-driven reduced order bases in the latent space of the autoencoders.
