Data-driven fluid mechanics: combining first principles and machine learning : based on a von Karman Institute lecture series
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...
Gespeichert in:
| Weitere Verfasser: | , , , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cambridge ; New York ; Port Melbourne ; New Delhi ; Singapore
Cambridge University Press
2023
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| Schlagworte: | |
| Zusammenfassung: | 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 |
| Beschreibung: | xviii, 448 Seiten Illustrationen, Diagramme |
| ISBN: | 9781108842143 |
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| id | DE-604.BV048991139 |
| illustrated | Illustrated |
| index_date | 2024-07-03T22:07:11Z |
| indexdate | 2024-07-10T09:52:11Z |
| institution | BVB |
| isbn | 9781108842143 |
| language | English |
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| physical | xviii, 448 Seiten Illustrationen, Diagramme |
| publishDate | 2023 |
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| publisher | Cambridge University Press |
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| spelling | Data-driven fluid mechanics combining first principles and machine learning : based on a von Karman Institute lecture series edited by Miguel A. Mendez (von Karman Institute for Fluid Dynamics, Belgium), Andrea Ianiro (Universidad Carlos III de Madrid), Bernd R. Noack (Harbin Institute of Technology, China), Steven L. Brunton (University of Washington) Cambridge ; New York ; Port Melbourne ; New Delhi ; Singapore Cambridge University Press 2023 xviii, 448 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier 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 Fluid mechanics / Data processing Strömungsmechanik (DE-588)4077970-1 gnd rswk-swf Big Data (DE-588)4802620-7 gnd rswk-swf Strömungsmechanik (DE-588)4077970-1 s Big Data (DE-588)4802620-7 s DE-604 Mendez, Miguel Alfonso 1988- (DE-588)1281465135 edt Ianiro, Andrea (DE-588)1281465321 edt Noack, Bernd R. 1966- (DE-588)172554446 edt Brunton, Steven L. 1984- (DE-588)1125029617 edt Erscheint auch als Online-Ausgabe 978-1-108-89621-4 |
| spellingShingle | Data-driven fluid mechanics combining first principles and machine learning : based on a von Karman Institute lecture series Fluid mechanics / Data processing Strömungsmechanik (DE-588)4077970-1 gnd Big Data (DE-588)4802620-7 gnd |
| subject_GND | (DE-588)4077970-1 (DE-588)4802620-7 |
| title | Data-driven fluid mechanics combining first principles and machine learning : based on a von Karman Institute lecture series |
| title_auth | Data-driven fluid mechanics combining first principles and machine learning : based on a von Karman Institute lecture series |
| title_exact_search | Data-driven fluid mechanics combining first principles and machine learning : based on a von Karman Institute lecture series |
| title_exact_search_txtP | Data-driven fluid mechanics combining first principles and machine learning : based on a von Karman Institute lecture series |
| title_full | Data-driven fluid mechanics combining first principles and machine learning : based on a von Karman Institute lecture series edited by Miguel A. Mendez (von Karman Institute for Fluid Dynamics, Belgium), Andrea Ianiro (Universidad Carlos III de Madrid), Bernd R. Noack (Harbin Institute of Technology, China), Steven L. Brunton (University of Washington) |
| title_fullStr | Data-driven fluid mechanics combining first principles and machine learning : based on a von Karman Institute lecture series edited by Miguel A. Mendez (von Karman Institute for Fluid Dynamics, Belgium), Andrea Ianiro (Universidad Carlos III de Madrid), Bernd R. Noack (Harbin Institute of Technology, China), Steven L. Brunton (University of Washington) |
| title_full_unstemmed | Data-driven fluid mechanics combining first principles and machine learning : based on a von Karman Institute lecture series edited by Miguel A. Mendez (von Karman Institute for Fluid Dynamics, Belgium), Andrea Ianiro (Universidad Carlos III de Madrid), Bernd R. Noack (Harbin Institute of Technology, China), Steven L. Brunton (University of Washington) |
| title_short | Data-driven fluid mechanics |
| title_sort | data driven fluid mechanics combining first principles and machine learning based on a von karman institute lecture series |
| title_sub | combining first principles and machine learning : based on a von Karman Institute lecture series |
| topic | Fluid mechanics / Data processing Strömungsmechanik (DE-588)4077970-1 gnd Big Data (DE-588)4802620-7 gnd |
| topic_facet | Fluid mechanics / Data processing Strömungsmechanik Big Data |
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