{"product_id":"uncertainty-quantification-and-predictive-computational-science-a-foundation-for-physical-scientists-and-engineers-hardcover","title":"Uncertainty Quantification and Predictive Computational Science: A Foundation for Physical Scientists and Engineers - Hardcover","description":"\u003cp\u003eby \u003cb\u003eRyan G. McClarren\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eThis textbook teaches the essential background and skills for understanding and quantifying uncertainties in a computational simulation, and for predicting the behavior of a system under those uncertainties. It addresses a critical knowledge gap in the widespread adoption of simulation in high-consequence decision-making throughout the engineering and physical sciences.\u003c\/p\u003e \u003cp\u003eConstructing sophisticated techniques for prediction from basic building blocks, the book first reviews the fundamentals that underpin later topics of the book including probability, sampling, and Bayesian statistics. Part II focuses on applying Local Sensitivity Analysis to apportion uncertainty in the model outputs to sources of uncertainty in its inputs. Part III demonstrates techniques for quantifying the impact of parametric uncertainties on a problem, specifically how input uncertainties affect outputs. The final section covers techniques for applying uncertainty quantification to make predictions under uncertainty, including treatment of epistemic uncertainties. It presents the theory and practice of predicting the behavior of a system based on the aggregation of data from simulation, theory, and experiment.\u003c\/p\u003e \u003cp\u003eThe text focuses on simulations based on the solution of systems of partial differential equations and includes in-depth coverage of Monte Carlo methods, basic design of computer experiments, as well as regularized statistical techniques. Code references, in python, appear throughout the text and online as executable code, enabling readers to perform the analysis under discussion. Worked examples from realistic, model problems help readers understand the mechanics of applying the methods. Each chapter ends with several assignable problems. \u003cbr\u003e\u003c\/p\u003e \u003cp\u003e\u003cem\u003eUncertainty Quantification and Predictive Computational Scienc\u003c\/em\u003ee fills the growing need for a classroom text for senior undergraduate and early-career graduate students in the engineering and physical sciences and supports independent study by researchers and professionals who must include uncertainty quantification and predictive science in the simulations they develop and\/or perform.\u003c\/p\u003e\u003ch3\u003eBack Jacket\u003c\/h3\u003e\u003cp\u003eThis textbook teaches the essential background and skills for understanding and quantifying uncertainties in a computational simulation, and for predicting the behavior of a system under those uncertainties. It addresses a critical knowledge gap in the widespread adoption of simulation in high-consequence decision-making throughout the engineering and physical sciences.Constructing sophisticated techniques for prediction from basic building blocks, the book first reviews the fundamentals that underpin later topics of the book including probability, sampling, and Bayesian statistics. Part II focuses on applying local sensitivity analysis to apportion uncertainty in the model outputs to sources of uncertainty in its inputs. Part III demonstrates techniques for quantifying the impact of parametric uncertainties on a problem, specifically how input uncertainties affect outputs. The final section covers techniques for applying uncertainty quantification to make predictions under uncertainty, including treatment of epistemic uncertainties. It presents the theory and practice of predicting the behavior of a system based on the aggregation of data from simulation, theory, and experiment.\u003cbr\u003eThe text focuses on simulations based on the solution of systems of partial differential equations and includes in-depth coverage of Monte Carlo methods, basic design of computer experiments, as well as regularized statistical techniques. Code references, in R and python, appear throughout the text and online as executable code, enabling readers to perform the analysis under discussion. Worked examples from realistic, model problems help readers understand the mechanics of applying the methods. Each chapter ends with several assignable problems. \u003ci\u003eUncertainty Quantification and Predictive Computational Scienc\u003c\/i\u003ee fills the growing need for a classroom text for senior undergraduate and first year graduate students in the engineering and physical sciences and supports independent study by researchers and professionals who must include uncertainty quantification and predictive science in the simulations they develop and\/or perform.\u003cbr\u003e \u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eOrganizes interdisciplinary topics of uncertainty quantification into a single teaching text\u003cbr\u003e\n\u003c\/li\u003e\n\u003cli\u003eReviews the fundamentals of probability and statistics \u003cbr\u003e\n\u003c\/li\u003e\n\u003cli\u003eGuides the transition from merely performing calculations to making confident predictions\u003cbr\u003e\n\u003c\/li\u003e\n\u003cli\u003eBuilds readers' confidence in the validity of their simulations \u003cbr\u003e\n\u003c\/li\u003e\n\u003cli\u003eIllustrates concepts with real-world examples and models from the physical sciences and engineering \u003cbr\u003e\n\u003c\/li\u003e\n\u003cli\u003eIncludes R and python code, enabling readers to perform the analysis\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eAuthor Biography\u003c\/h3\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eRyan McClarren has been teaching uncertainty quantification and predictive computational science to students from various engineering and physical science departments at since 2009. He is currently Associate Professor of Aerospace and Mechanical Engineering at the University of Notre Dame. Prior to joining Notre Dame in 2017, he was Assistant Professor of Nuclear Engineering at Texas A\u0026amp;M University, an institution well-known in the nuclear engineering community for its computational research and education. He has authored numerous publications in refereed journals, is the author of a book that teaches python and numerical methods to undergraduates, \u003ci\u003eComputational Nuclear Engineering and Radiological Science Using Python, \u003c\/i\u003eand was the editor of a special issue of the journal Transport Theory and Statistical Physics. A well-known member of the computational nuclear engineering community, he has won research awards from NSF, DOE, and three national labs. While an undergraduate at the University of Michigan he won three awards for creative writing. Before joining the faculty of Texas A\u0026amp;M, Dr. McClarren was a research scientist at Los Alamos National Laboratory in the Computational Physics and Methods group.\u003c\/p\u003e\u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 345\u003c\/div\u003e\u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 0.81 x 9.21 x 6.14 IN\u003c\/div\u003e\u003cdiv\u003e\n\u003cstrong\u003eIllustrated:\u003c\/strong\u003e Yes\u003c\/div\u003e\u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e December 05, 2018\u003c\/div\u003e","brand":"Books by splitShops","offers":[{"title":"Default Title","offer_id":47416622416050,"sku":"9783319995243","price":178.18,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0770\/3891\/1666\/files\/5405c6d79eec4e17cc9d7e87ba02983e.webp?v=1778411014","url":"https:\/\/box.dadyminds.org\/products\/uncertainty-quantification-and-predictive-computational-science-a-foundation-for-physical-scientists-and-engineers-hardcover","provider":"DADYMINDS BOX","version":"1.0","type":"link"}