Books > Science & Mathematics > Physics > Classical mechanics > Fluid mechanics
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Data-Driven Fluid Mechanics - Combining First Principles and Machine Learning (Hardcover)
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Data-Driven Fluid Mechanics - Combining First Principles and Machine Learning (Hardcover)
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Data-driven methods have become an essential part of the
methodological portfolio of fluid dynamicists, motivating students
and practitioners to gather practical knowledge from a diverse
range of disciplines. These fields include computer science,
statistics, optimization, signal processing, pattern recognition,
nonlinear dynamics, and control. Fluid mechanics is historically a
big data field and offers a fertile ground for developing and
applying data-driven methods, while also providing valuable
shortcuts, constraints, and interpretations based on its powerful
connections to basic physics. Thus, hybrid approaches that leverage
both methods based on data as well as fundamental principles are
the focus of active and exciting research. Originating from a
one-week lecture series course by the von Karman Institute for
Fluid Dynamics, this book presents an overview and a pedagogical
treatment of some of the data-driven and machine learning tools
that are leading research advancements in model-order reduction,
system identification, flow control, and data-driven turbulence
closures.
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