Physical Review Journal Club: Machine Learning in Fluid Dynamics: A Critical Assessment скачать в хорошем качестве

Physical Review Journal Club: Machine Learning in Fluid Dynamics: A Critical Assessment

Machine learning (ML) has become increasingly popular in the fluid dynamics community, offering new tools for analyzing, modeling, predicting, and controlling a wide range of flows. In their article, Kunihiko “Sam” Taira and co-authors provide a critical assessment of the opportunities and limitations of ML. While it has in some cases outperformed traditional approaches, many fundamental challenges remain; tackling them is necessary both to deepen our understanding of flow physics, and to extend the applicability of ML beyond fundamental research. To accelerate progress in the field, the authors highlight the importance of community-maintained datasets and open-source code repositories, as well as the need for effective training, both for early-career and well-established researchers. This article aims to spark discussions and foster collaborative efforts toward a more robust integration of ML into fluid dynamics research: https://journals.aps.org/prfluids/abs...