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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.

Fluid mechanics --- Dinàmica de fluids computacional --- Data processing. --- Mecànica de fluids --- Informàtica

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Mecànica de fluids --- Hidromecànica --- Mecànica dels medis continus --- Dinàmica de fluids --- Hidràulica --- Hidrodinàmica --- Fluids --- Fluid mechanics. --- Fluid mechanics --- Data processing. --- Hydromechanics --- Continuum mechanics

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This book is an introduction to the use of machine learning and data-driven approaches in fluid simulation and animation, as an alternative to traditional modeling techniques based on partial differential equations and numerical methods – and at a lower computational cost. This work starts with a brief review of computability theory, aimed to convince the reader – more specifically, researchers of more traditional areas of mathematical modeling – about the power of neural computing in fluid animations. In these initial chapters, fluid modeling through Navier-Stokes equations and numerical methods are also discussed. The following chapters explore the advantages of the neural networks approach and show the building blocks of neural networks for fluid simulation. They cover aspects related to training data, data augmentation, and testing. The volume completes with two case studies, one involving Lagrangian simulation of fluids using convolutional neural networks and the other using Generative Adversarial Networks (GANs) approaches.

Differential equations. --- Artificial intelligence. --- Soft condensed matter. --- Computer simulation. --- Differential Equations. --- Artificial Intelligence. --- Fluids. --- Computer Modelling. --- Deep learning (Machine learning) --- Aprenentatge automàtic --- Mecànica de fluids --- Simulació per ordinador

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Fluid mechanics. --- Fluid mechanics --- Thermodynamics. --- Mathematical models. --- Chemistry, Physical and theoretical --- Dynamics --- Mechanics --- Physics --- Heat --- Heat-engines --- Quantum theory --- Hydromechanics --- Continuum mechanics --- Mecànica de fluids --- Hidromecànica --- Mecànica dels medis continus --- Dinàmica de fluids --- Hidràulica --- Hidrodinàmica --- Fluids

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This title provides an up-to-date, lively and approachable introduction to the mathematical formalism, numerical techniques and applications of relativistic hydrodynamics. The topic is presented here in a form which will be appreciated both by students and researchers in the field. The book is especially recommended to astrophysicists, particle physicists and applied mathematicians.

Relativistic fluid dynamics. --- Hydrodynamics. --- Fluid dynamics --- Relativistic mechanics --- Relativity (Physics) --- Relativistic fluid dynamics --- Hydrodynamics --- Hidrodinàmica --- Dinàmica de fluids --- Mecànica de fluids --- Aerodinàmica --- Capa límit --- Equacions de Navier-Stokes --- Fluídica --- Fluïdització --- Magnetohidrodinàmica --- Ones de xoc --- Turbulència --- Vòrtexs --- Tixotropia --- Enginyeria hidràulica --- Mecànica --- Mecànica analítica --- Cavitació --- Ones --- Ones de gravetat --- Urodinàmica --- Viscositat