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

@article{Fresca2021RealtimeSO,
  title={Real-time simulation of parameter-dependent fluid flows through deep learning-based reduced order models},
  author={Stefania Fresca and Andrea Manzoni},
  journal={ArXiv},
  year={2021},
  volume={abs/2106.05722}
}
Simulating fluid flows in different virtual scenarios is of key importance in engineering applications. However, high-fidelity, full-order models relying, e.g., on the finite element method, are unaffordable whenever fluid flows must be simulated in almost real-time. Reduced order models (ROMs) relying, e.g., on proper orthogonal decomposition (POD) provide reliable approximations to parameter-dependent fluid dynamics problems in rapid times. However, they might require expensive hyper… 

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