Machine Learning in Fluid Dynamics: Theory and Applications

Dear Colleagues,

Nowadays, artificial intelligence plays a vital role in learning and extracting patterns from complex data. Despite their immense success in other disciplines, machine leraning tecniques are just beginning to be applied in the field of fluid dynamics. Such data analytics and statistical tools have been utilized, for example, for the physical model inference, subgrid scale closure modeling, model reduction, and fast emulators for data assimilation, paramater estimation, uncertainlty quantification, control and optimization. However, most of the approaches so far are black boxes and their generalizability, interpretibility, robustness and numerical analysis remain an open challange. Therefore, we invite you to submit your contribution in all aspects of data-driven/scientific/statistical learning to this special issue.

Topics in this call include, but are not limited to: new learning algorithms, parallel computing implementations, numerical analysis, parameterizations and turbulence modeling, reduced order modeling, intrusive and non-intrusive model development, near real time predictions, digital twins, and hybrid frameworks between physics-based and data-driven approaches as well as their implementations to the problems ariasing in canonical or realistic fluid dynamics applications.

Dr. Omer San
Guest Editor

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