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Model Calibration and Parameter Estimation : For Environmental and Water Resource Systems

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概要 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.続きを見る
目次 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.
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本文を見る Full text available from Springer Mathematics and Statistics eBooks 2015 English/International

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登録日 2020.06.27
更新日 2020.06.28