![]() Over the past 50 years, many techniques were developed to handle such data, ranging from advanced algorithms for data processing and compression to fluid mechanics databases ( Perlman et al. 2016) due to high-performance computing architectures and advances in experimental measurement capabilities. Indeed, in the past few decades, big data have been a reality in fluid mechanics research ( Pollard et al. ML provides a powerful information-processing framework that can augment, and possibly even transform, current lines of fluid mechanics research and industrial applications.įluid mechanics has traditionally dealt with massive amounts of data from experiments, field measurements, and large-scale numerical simulations. The strengths and limitations of these methods are addressed from the perspective of scientific inquiry that considers data as an inherent part of modeling, experiments, and simulations. We outline fundamental ML methodologies and discuss their uses for understanding, modeling, optimizing, and controlling fluid flows. This article presents an overview of past history, current developments, and emerging opportunities of ML for fluid mechanics. Moreover, ML algorithms can augment domain knowledge and automate tasks related to flow control and optimization. Machine learning (ML) offers a wealth of techniques to extract information from data that can be translated into knowledge about the underlying fluid mechanics. The field of fluid mechanics is rapidly advancing, driven by unprecedented volumes of data from experiments, field measurements, and large-scale simulations at multiple spatiotemporal scales. ![]()
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