Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control, 2/e (Hardcover)

Steven L. Brunton

  • 出版商: Camberidge
  • 出版日期: 2022-05-05
  • 售價: $1,560
  • 貴賓價: 9.8$1,529
  • 語言: 英文
  • 頁數: 614
  • ISBN: 1009098489
  • ISBN-13: 9781009098489
  • 相關分類: Machine Learning
  • 立即出貨 (庫存=1)

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Review

'Finally, a book that introduces data science in a context that will make any mechanical engineer feel comfortable. Data science is the new calculus, and no engineer should graduate without a thorough understanding of the topic.' Hod Lipson, Columbia University

'This book is a must-have for anyone interested in data-driven modeling and simulations. The readers as diverse as undergraduate STEM students and seasoned researchers would find it useful as a guide to this rapidly evolving field. Topics covered by the monograph include dimension reduction, machine learning, and robust control of dynamical systems with uncertain/random inputs. Every chapter contains codes and homework problems, which make this treaties ideal for the classroom setting. The book is supplemented with online lectures, which are not only educational but also entertaining to watch.' Daniel M. Tartakovsky, Stanford University

'Engineering principles will always be based on physics, and the models that underpin engineering will be derived from these physical laws. But in the future models based on relationships in large datasets will be as important and, when used alongside physics-based models, will lead to new insights and designs. Brunton and Kutz will equip students and practitioners with the tools they will need for this exciting future.' Greg Hyslop, Boeing

'Brunton and Kutz's book is fast becoming an indispensable resource for machine learning and data-driven learning in science and engineering. The second edition adds several timely topics in this lively field, including reinforcement learning and physics-informed machine learning. The text balances theoretical foundations and concrete examples with code, making it accessible and practical for students and practitioners alike.' Tim Colonius, California Institute of Technology

'This is a must read for those who are interested in understanding what machine learning can do for dynamical systems! Steve and Nathan have done an excellent job in bringing everyone up to speed to the modern application of machine learning on these complex dynamical systems.' Shirley Ho, Flatiron Institute/New York University

Book Description

A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®.

商品描述(中文翻譯)

評論

「終於有一本書以讓任何機械工程師都能感到舒適的背景介紹數據科學。數據科學是新的微積分,沒有一位工程師應該在沒有對這個主題有透徹的理解之前畢業。」哥倫比亞大學的霍德·利普森(Hod Lipson)

「這本書對於任何對數據驅動建模和模擬感興趣的人來說都是必備的。從本書中,從本科理工科學生到經驗豐富的研究人員都會發現它作為這個快速發展領域的指南非常有用。專著涵蓋的主題包括降維、機器學習以及具有不確定/隨機輸入的動態系統的強健控制。每一章都包含代碼和作業問題,使得這本專著在課堂環境中非常理想。該書還配有在線講座,這些講座不僅具有教育性,而且觀看起來也很有趣。」斯坦福大學的丹尼爾·M·塔塔科夫斯基(Daniel M. Tartakovsky)

「工程原則將永遠基於物理學,支撐工程的模型將來自這些物理定律。但在未來,基於大數據集中的關係的模型將同樣重要,並且在與基於物理模型並用時,將帶來新的見解和設計。布倫頓和庫茨將為學生和從業人員提供他們在這個令人興奮的未來所需的工具。」波音公司的格雷格·海斯洛普(Greg Hyslop)

「布倫頓和庫茨的書正在成為機器學習和科學工程中不可或缺的資源。第二版在這個活躍的領域中增加了幾個及時的主題,包括強化學習和物理信息機器學習。該書在理論基礎和具體示例之間取得了平衡,並提供了代碼,使其對學生和從業人員都具有可訪問性和實用性。」加州理工學院的蒂姆·科洛尼厄斯(Tim Colonius)

「對於那些有興趣了解機器學習對動態系統的應用的人來說,這是一本必讀之作!史蒂夫和納森在將每個人帶入這些複雜動態系統的現代機器學習應用方面做得非常出色。」Flatiron Institute/New York University的雪莉·何(Shirley Ho)

書籍描述

這是一本涵蓋工程和科學中的建模和控制的數據科學和機器學習方法的教科書,使用Python和MATLAB®。