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Simulating fluid flows in different virtual scenarios is of key importance inengineering applications. However, high-fidelity, full-order models relying,e.g., on the finite element method, are unaffordable whenever fluid flows mustbe simulated in almost real-time. Reduced order models (ROMs) relying, e.g., onproper orthogonal decomposition (POD) provide reliable approximations toparameter-dependent fluid dynamics problems in rapid times. However, they mightrequire expensive hyper-reduction strategies for handling parameterizednonlinear terms, and enriched reduced spaces (or Petrov-Galerkin projections)if a mixed velocity-pressure formulation is considered, possibly hampering theevaluation of reliable solutions in real-time. Dealing with fluid-structureinteractions entails even higher difficulties. The proposed deep learning(DL)-based ROMs overcome all these limitations by learning in a non-intrusiveway both the nonlinear trial manifold and the reduced dynamics. To do so, theyrely on deep neural networks, after performing a former dimensionalityreduction through POD enhancing their training times substantially. Theresulting POD-DL-ROMs are shown to provide accurate results in almost real-timefor the flow around a cylinder benchmark, the fluid-structure interactionbetween an elastic beam attached to a fixed, rigid block and a laminarincompressible flow, and the blood flow in a cerebral aneurysm.

Real-time simulation of parameter-dependent fluid flows through deep learning-based reduced order models