Machine Learning for Engineers: Using data to solve problems for physical systems

Author:   Ryan G. McClarren
Publisher:   Springer Nature Switzerland AG
Edition:   2021 ed.
ISBN:  

9783030703905


Pages:   247
Publication Date:   23 September 2022
Format:   Paperback
Availability:   Manufactured on demand   Availability explained
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Machine Learning for Engineers: Using data to solve problems for physical systems


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Author:   Ryan G. McClarren
Publisher:   Springer Nature Switzerland AG
Imprint:   Springer Nature Switzerland AG
Edition:   2021 ed.
Weight:   0.409kg
ISBN:  

9783030703905


ISBN 10:   3030703908
Pages:   247
Publication Date:   23 September 2022
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand   Availability explained
We will order this item for you from a manufactured on demand supplier.

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Ryan McClarren, Associate Professor of Aerospace and Mechanical Engineering at the University of Notre Dame, has applied machine learning to understand, analyze, and optimize engineering systems throughout his academic career. He has authored numerous publications in refereed journals on machine learning, uncertainty quantification, and numerical methods, as well as two scientific texts: Uncertainty Quantification and Predictive Computational Science: A Foundation for Physical Scientists and Engineers and Computational Nuclear Engineering and Radiological Science Using Python.  A well-known member of the computational engineering community, Dr. McClarren has won research awards from NSF, DOE, and three national labs. Prior to joining Notre Dame in 2017, he was Assistant Professor of Nuclear Engineering at Texas A&M University, and previously a research scientist at Los Alamos National Laboratory in the Computational Physics and Methods group. While an undergraduate at the University of Michigan he won three awards for creative writing. 

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