Data-Driven Fluid Mechanics: Combining First Principles and Machine Learning

doi:10.1017/9781108896214

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Data-Driven Fluid Mechanics

Data-Driven Fluid Mechanics

Combining First Principles and Machine Learning

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Stavrogiannis, Christos Sofos, Filippos Sagri, Maria Vavougios, Denis and Karakasidis, Theodoros E. 2023. Twofold Machine-Learning and Molecular Dynamics: A Computational Framework. Computers, Vol. 13, Issue. 1, p. 2.

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Vignon, Colin Rabault, Jean Vasanth, Joel Alcántara-Ávila, Francisco Mortensen, Mikael and Vinuesa, Ricardo 2023. Effective control of two-dimensional Rayleigh–Bénard convection: Invariant multi-agent reinforcement learning is all you need. Physics of Fluids, Vol. 35, Issue. 6,

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Teutsch, Philipp Käufer, Theo Mäder, Patrick and Cierpka, Christian 2023. Data-driven estimation of scalar quantities from planar velocity measurements by deep learning applied to temperature in thermal convection. Experiments in Fluids, Vol. 64, Issue. 12,

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Alam, Jahrul M 2023. Wavelet Transforms and Machine Learning Methods for the Study of Turbulence. Fluids, Vol. 8, Issue. 8, p. 224.

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Liao, Zi-Mo Zhao, Zhiye Chen, Liang-Bing Wan, Zhen-Hua Liu, Nan-Sheng and Lu, Xi-Yun 2023. Reduced-order variational mode decomposition to reveal transient and non-stationary dynamics in fluid flows. Journal of Fluid Mechanics, Vol. 966, Issue. ,

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Basu, Ankan Saha, Aritra Banerjee, Sumanta Roy, Prokash C. and Kundu, Balaram 2024. A Review of Artificial Intelligence Methods in Predicting Thermophysical Properties of Nanofluids for Heat Transfer Applications. Energies, Vol. 17, Issue. 6, p. 1351.

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Edited by Miguel A. Mendez, Von Karman Institute for Fluid Dynamics, Belgium, Andrea Ianiro, Universidad Carlos III de Madrid, Bernd R. Noack, Harbin Institute of Technology, China, Steven L. Brunton, University of Washington

Publisher:: Cambridge University Press
Online publication date:: January 2023
Print publication year:: 2023
Online ISBN:: 9781108896214
DOI:: https://doi.org/10.1017/9781108896214

Subjects:: Mathematics, Engineering, Fluid Dynamics and Solid Mechanics, Thermal-Fluids Engineering

59.99 (GBP)

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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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Data-Driven Fluid Mechanics

Combining First Principles and Machine Learning

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