Prospects of federated machine learning in fluid dynamics. (arXiv:2208.07017v1 [cs.LG])

Physics-based models have been mainstream in fluid dynamics for developing
predictive models. In recent years, machine learning has offered a renaissance
to the fluid community due to the rapid developments in data science,
processing units, neural network based technologies, and sensor adaptations. So
far in many applications in fluid dynamics, machine learning approaches have
been mostly focused on a standard process that requires centralizing the
training data on a designated machine or in a data center. In this letter, we …

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