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OverviewFull Product DetailsAuthor: Ryan G. McClarrenPublisher: Springer Nature Switzerland AG Imprint: Springer Nature Switzerland AG Edition: 2021 ed. Weight: 0.409kg ISBN: 9783030703905ISBN 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 ![]() We will order this item for you from a manufactured on demand supplier. Table of ContentsReviewsAuthor InformationRyan 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. Tab Content 6Author Website:Countries AvailableAll regions |