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Data-Driven Fluid Mechanics: Combining First Principles and Machine Learning
Data-Driven Fluid Mechanics: Combining First Principles and Machine Learning
Big data and machine learning are driving profound technological progress across nearly every industry, and are rapidly shaping fluid mechanics research. This is a self-contained and pedagogical treatment of the data-driven tools that are leading research in model-order reduction, system identification, flow control, and turbulence closures.
468 pages, Worked examples or Exercises
| 介质类型 | 图书 Hardcover Book (精装硬皮书) |
| 已发行 | 2023年2月2日 |
| ISBN13 | 9781108842143 |
| 出版商 | Cambridge University Press |
| 页数 | 468 |
| 商品尺寸 | 252 × 176 × 27 mm · 962 g |
| 语言 | 英语 |
| 编辑 | Brunton, Steven L. (University of Washington) |
| 编辑 | Ianiro, Andrea (Universidad Carlos III de Madrid) |
| 编辑 | Mendez, Miguel A. (Von Karman Institute for Fluid Dynamics, Belgium) |
| 编辑 | Noack, Bernd R. (Harbin Institute of Technology, China) |