Data-driven fluid mechanics: combining first principles and machine learning (Record no. 27830)

000 -LEADER
fixed length control field 01814 a2200253 4500
003 - CONTROL NUMBER IDENTIFIER
control field OSt
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20230707163639.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
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020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781108842143
040 ## - CATALOGING SOURCE
Original cataloging agency ICTS-TIFR
050 ## - LIBRARY OF CONGRESS CALL NUMBER
Classification number QA901 .D375
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Miguel A. Mendez
245 ## - TITLE STATEMENT
Title Data-driven fluid mechanics: combining first principles and machine learning
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Name of publisher, distributor, etc. Cambridge University Press
Place of publication, distribution, etc. Cambridge, UK
Date of publication, distribution, etc. 2023
300 ## - Physical Description
Pages: xviii, 448 p.
490 ## - SERIES STATEMENT
Series statement based on a von Karman Institute lecture series
520 ## - SUMMARY, ETC.
Summary, etc. 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.
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Fluid mechanics
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Data processing
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name Andrea Ianiro
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name Bernd R. Noack
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name Steven L. Brunton
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme
Koha item type Book
Holdings
Withdrawn status Lost status Damaged status Not for loan Home library Shelving location Date acquired Inventory number Full call number Accession No. Checked out Koha item type
        ICTS Rack No 8 04/27/2023 53393 QA901 .D375 02638 12/30/2024 Book