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Model Calibration and Parameter Estimation = For Environmental and Water Resource Systems /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Model Calibration and Parameter Estimation/ by Ne-Zheng Sun, Alexander Sun.
其他題名:	For Environmental and Water Resource Systems /
作者:	Sun, Ne-Zheng.
其他作者:	Sun, Alexander.
面頁冊數:	XXVIII, 621 p. 123 illus., 107 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Mathematical models. -
電子資源:	https://doi.org/10.1007/978-1-4939-2323-6
ISBN:	9781493923236

Model Calibration and Parameter Estimation = For Environmental and Water Resource Systems /
Sun, Ne-Zheng.

Model Calibration and Parameter EstimationFor Environmental and Water Resource Systems /[electronic resource] :by Ne-Zheng Sun, Alexander Sun. - 1st ed. 2015. - XXVIII, 621 p. 123 illus., 107 illus. in color.online resource.

Introduction -- The Classical Inverse Problem -- The Gauss-Newton Method -- Multiobjective Inversion and Regularization -- Statistical Methods for Parameter Estimation -- Model Differentiation -- Model Dimension Reduction -- Development of Data-Driven Models -- Data Assimilation for Inversion -- Model Uncertainty Quantification -- Optimal Experimental Design -- Goal-Oriented Modeling.

This three-part book provides a comprehensive and systematic introduction to the development of useful models for complex systems. Part 1 covers the classical inverse problem for parameter estimation in both deterministic and statistical frameworks, Part 2 is dedicated to system identification, hyperparameter estimation, and model dimension reduction, and Part 3 considers how to collect data and construct reliable models for prediction and decision-making. For the first time, topics such as multiscale inversion, stochastic field parameterization, level set method, machine learning, global sensitivity analysis, data assimilation, model uncertainty quantification, robust design, and goal-oriented modeling, are systematically described and summarized in a single book from the perspective of model inversion, and elucidated with numerical examples from environmental and water resources modeling. Readers of this book will not only learn basic concepts and methods for simple parameter estimation, but also get familiar with advanced methods for modeling complex systems. Algorithms for mathematical tools used in this book, such as numerical optimization, automatic differentiation, adaptive parameterization, hierarchical Bayesian, metamodeling, Markov chain Monte Carlo, are covered in details. This book can useful for graduate and upper level undergraduate students majoring in environmental engineering, hydrology, and geosciences. It also serves as an essential reference book for petroleum engineers, mining engineers, chemists, mechanical engineers, ecologists, biomedical engineers, applied mathematicians, and others who perform mathematical modeling.

ISBN: 9781493923236

Standard No.: 10.1007/978-1-4939-2323-6doiSubjects--Topical Terms:

527886
Mathematical models.

LC Class. No.: TA342-343

Dewey Class. No.: 003.3

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520 $a This three-part book provides a comprehensive and systematic introduction to the development of useful models for complex systems. Part 1 covers the classical inverse problem for parameter estimation in both deterministic and statistical frameworks, Part 2 is dedicated to system identification, hyperparameter estimation, and model dimension reduction, and Part 3 considers how to collect data and construct reliable models for prediction and decision-making. For the first time, topics such as multiscale inversion, stochastic field parameterization, level set method, machine learning, global sensitivity analysis, data assimilation, model uncertainty quantification, robust design, and goal-oriented modeling, are systematically described and summarized in a single book from the perspective of model inversion, and elucidated with numerical examples from environmental and water resources modeling. Readers of this book will not only learn basic concepts and methods for simple parameter estimation, but also get familiar with advanced methods for modeling complex systems. Algorithms for mathematical tools used in this book, such as numerical optimization, automatic differentiation, adaptive parameterization, hierarchical Bayesian, metamodeling, Markov chain Monte Carlo, are covered in details. This book can useful for graduate and upper level undergraduate students majoring in environmental engineering, hydrology, and geosciences. It also serves as an essential reference book for petroleum engineers, mining engineers, chemists, mechanical engineers, ecologists, biomedical engineers, applied mathematicians, and others who perform mathematical modeling.
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