BIBLIO is the largest independent book marketplace in the world, with over 100 million books.

Skip to content

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

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

Data-Driven Fluid Mechanics: Combining First Principles and Machine Learning
Stock photo: cover may vary

Data-Driven Fluid Mechanics: Combining First Principles and Machine Learning Hardback - 2023

by Miguel A. Mendez

Add to wish list
  • Used
New

Description

like new.
Ask the seller a question Add to wish list
A$245.33
A$5.66 Delivery within USA
Standard delivery: 2 to 14 days
More delivery options
Ships from GreatBookPrices (Maryland, United States)

Details

  • Title Data-Driven Fluid Mechanics: Combining First Principles and Machine Learning
  • Author Miguel A. Mendez
  • Binding Hardback
  • Condition New
  • Pages 468
  • Volumes 1
  • Language ENG
  • Publisher Cambridge University Press
  • Publication date 2023-02-02
  • Bookseller's Inventory # 44569914
  • ISBN 9781108842143 / 1108842143
  • Weight 2.25 lbs (1.02 kg)
  • Dimensions 9.29 x 6.3 x 0.39 in (23.60 x 16.00 x 0.99 cm)
  • Category Science
  • Quantity available 5

About GreatBookPrices Maryland, United States

Biblio member since 2024

Since 1991, we have worked every day to serve our customers with state-of-the-art technology and world class service. We are dedicated to providing customers around the world with the widest selection of books, DVDs, and CDs at the absolute lowest price.

Terms of Sale: 30 day return guarantee, with full refund including original shipping costs for up to 30 days after delivery if an item arrives misdescribed or damaged.

Browse books from GreatBookPrices

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

From the publisher

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.
tracking-