| 000 -LEADER |
|---|
| fixed length control field |
01823 a2200229 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
|---|
| control field |
OSt |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| ISBN |
9781108842143 |
| 040 ## - CATALOGING SOURCE |
|---|
| Transcribing agency |
IIT Kanpur |
| 041 ## - LANGUAGE CODE |
|---|
| Language code of text/sound track or separate title |
eng |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
532.00285 |
| Item number |
D262 |
| 245 ## - TITLE STATEMENT |
|---|
| Title |
Data-driven fluid mechanics |
| Remainder of title |
combining first principles and machine learning |
| Statement of responsibility, etc |
edited by Miguel A. Mendez ...[et al.] |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
|---|
| Name of publisher |
Cambridge University Press |
| Year of publication |
2023 |
| Place of publication |
Cambridge |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Number of Pages |
xviii, 448p |
| 440 ## - SERIES STATEMENT/ADDED ENTRY--TITLE |
|---|
| Title |
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"-- Provided by publisher. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical Term |
Fluid mechanics -- Data processing |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Mendez, Miguel Alfonso [ed.] |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Ianiro, Andrea [ed.] |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Noack, Bernd R. [ed.] |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Brunton, Steven L. [ed.] |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Koha item type |
Books |
