Data-driven fluid mechanics : (Record no. 84271)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 02511nam a2200361 i 4500 |
| 001 - CONTROL NUMBER | |
| control field | CR9781108896214 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20240730160810.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 200108s2023||||enk o ||1 0|eng|d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9781108896214 (ebook) |
| 082 00 - CLASSIFICATION NUMBER | |
| Call Number | 532 |
| 245 00 - TITLE STATEMENT | |
| Title | Data-driven fluid mechanics : |
| Sub Title | combining first principles and machine learning : based on a von Karman Institute lecture series / |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | 1 online resource (xviii, 448 pages) : |
| 500 ## - GENERAL NOTE | |
| Remark 1 | Title from publisher's bibliographic system (viewed on 12 Jan 2023). |
| 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 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| General subdivision | Data processing. |
| 700 1# - AUTHOR 2 | |
| Author 2 | Mendez, Miguel Alfonso, |
| 700 1# - AUTHOR 2 | |
| Author 2 | Ianiro, Andrea, |
| 700 1# - AUTHOR 2 | |
| Author 2 | Noack, Bernd R., |
| 700 1# - AUTHOR 2 | |
| Author 2 | Brunton, Steven L. |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | https://doi.org/10.1017/9781108896214 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | eBooks |
| 264 #1 - | |
| -- | Cambridge : |
| -- | Cambridge University Press, |
| -- | 2023. |
| 336 ## - | |
| -- | text |
| -- | txt |
| -- | rdacontent |
| 337 ## - | |
| -- | computer |
| -- | c |
| -- | rdamedia |
| 338 ## - | |
| -- | online resource |
| -- | cr |
| -- | rdacarrier |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Fluid mechanics |
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