| 000 -LEADER |
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| fixed length control field |
a |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
230904b xxu||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
9781108842143 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
532.05015194 |
| Item number |
MEN |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Mendez, Miguel Alfonso |
| Relator term |
ed. |
| 245 ## - TITLE STATEMENT |
|---|
| Title |
Data-driven fluid mechanics : combining first principles and machine learning |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
|---|
| Name of publisher, distributor, etc |
Cambridge University Press, |
| Date of publication, distribution, etc |
2023 |
| Place of publication, distribution, etc |
Cambridge : |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xviii, 448 p. ; |
| Other physical details |
ill., (some col.), |
| Dimensions |
25 cm |
| 365 ## - TRADE PRICE |
|---|
| Price amount |
59.99 |
| Price type code |
GBP |
| Unit of pricing |
110.40 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc |
Includes bibliographical references. |
| 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 as entry element |
Fluid mechanics |
|
|---|
| Topical term or geographic name as entry element |
Data processing |
|
|---|
| Topical term or geographic name as entry element |
TimeFrequency Analysis |
|
|---|
| Topical term or geographic name as entry element |
Proper Orthogonal Decomposition |
|
|---|
| Topical term or geographic name as entry element |
Koopman Theory |
|
|---|
| Topical term or geographic name as entry element |
Multiscale Modal Analysis |
|
|---|
| Topical term or geographic name as entry element |
Data Driven Modal Analysis |
|
|---|
| Topical term or geographic name as entry element |
Linear Dynamical Systems |
|
|---|
| Topical term or geographic name as entry element |
ReducedOrder Modeling |
|
|---|
| Topical term or geographic name as entry element |
Turbulence Control |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Ianiro, Andrea |
| Relator term |
ed. |
|
|---|
| Personal name |
Noack, Bernd R. |
| Relator term |
ed. |
|
|---|
| Personal name |
Brunton, Steven L. |
| Relator term |
ed. |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
|
| Item type |
Books |