Parameter Estimation and Inverse Problems

Parameter Estimation and Inverse Problems, Second Edition provides geoscience students and professionals with answers to common questions like how one can derive a physical model from a finite set of observations containing errors, and how one may determine the quality of such a model. This book takes on these fundamental and challenging problems, introducing students and professionals to the broad range of approaches that lie in the realm of inverse theory. The authors present both the underlying theory and practical algorithms for solving inverse problems. The authors' treatment is appropriate for geoscience graduate students and advanced undergraduates with a basic working knowledge of calculus, linear algebra, and statistics. Parameter Estimation and Inverse Problems, Second Edition introduces readers to both Classical and Bayesian approaches to linear and nonlinear problems with particular attention paid to computational, mathematical, and statistical issues related to their application to geophysical problems. The textbook includes Appendices covering essential linear algebra, statistics, and notation in the context of the subject. Includes appendices for review of needed concepts in linear, statistics, and vector calculus. Accessible to students and professionals without a highly specialized mathematical background.

Produk Detail:

  • Author : Richard C. Aster
  • Publisher : Academic Press
  • Pages : 360 pages
  • ISBN : 0123850487
  • Rating : 4/5 from 21 reviews
CLICK HERE TO GET THIS BOOKParameter Estimation and Inverse Problems

Parameter Estimation and Inverse Problems

Parameter Estimation and Inverse Problems
  • Author : Richard C. Aster,Brian Borchers,Clifford H. Thurber
  • Publisher : Academic Press
  • Release : 14 August 2022
GET THIS BOOKParameter Estimation and Inverse Problems

Parameter Estimation and Inverse Problems, Second Edition provides geoscience students and professionals with answers to common questions like how one can derive a physical model from a finite set of observations containing errors, and how one may determine the quality of such a model. This book takes on these fundamental and challenging problems, introducing students and professionals to the broad range of approaches that lie in the realm of inverse theory. The authors present both the underlying theory and practical

Parameter Estimation and Inverse Problems

Parameter Estimation and Inverse Problems
  • Author : Richard C. Aster,Brian Borchers,Clifford H. Thurber
  • Publisher : Academic Press
  • Release : 10 December 2011
GET THIS BOOKParameter Estimation and Inverse Problems

Parameter Estimation and Inverse Problems, Second Edition provides geoscience students and professionals with answers to common questions like how one can derive a physical model from a finite set of observations containing errors, and how one may determine the quality of such a model. This book takes on these fundamental and challenging problems, introducing students and professionals to the broad range of approaches that lie in the realm of inverse theory. The authors present both the underlying theory and practical

Parameter Estimation and Inverse Problems

Parameter Estimation and Inverse Problems
  • Author : Richard C. Aster,Brian Borchers,Clifford H. Thurber
  • Publisher : Elsevier
  • Release : 16 October 2018
GET THIS BOOKParameter Estimation and Inverse Problems

Parameter Estimation and Inverse Problems, Third Edition, is structured around a course at New Mexico Tech and is designed to be accessible to typical graduate students in the physical sciences who do not have an extensive mathematical background. The book is complemented by a companion website that includes MATLAB codes that correspond to examples that are illustrated with simple, easy to follow problems that illuminate the details of particular numerical methods. Updates to the new edition include more discussions of

Inverse Problem Theory and Methods for Model Parameter Estimation

Inverse Problem Theory and Methods for Model Parameter Estimation
  • Author : Albert Tarantola
  • Publisher : SIAM
  • Release : 01 January 2005
GET THIS BOOKInverse Problem Theory and Methods for Model Parameter Estimation

While the prediction of observations is a forward problem, the use of actual observations to infer the properties of a model is an inverse problem. Inverse problems are difficult because they may not have a unique solution. The description of uncertainties plays a central role in the theory, which is based on probability theory. This book proposes a general approach that is valid for linear as well as for nonlinear problems. The philosophy is essentially probabilistic and allows the reader

Geophysical Inverse Theory

Geophysical Inverse Theory
  • Author : Robert L. Parker
  • Publisher : Princeton University Press
  • Release : 31 December 2019
GET THIS BOOKGeophysical Inverse Theory

In many physical sciences, the most natural description of a system is with a function of position or time. In principle, infinitely many numbers are needed to specify that function, but in practice only finitely many measurements can be made. Inverse theory concerns the mathematical techniques that enable researchers to use the available information to build a model of the unknown system or to determine its essential properties. In Geophysical Inverse Theory, Robert Parker provides a systematic development of inverse

Inverse Problem Theory

Inverse Problem Theory
  • Author : A. Tarantola
  • Publisher : Elsevier
  • Release : 14 October 2013
GET THIS BOOKInverse Problem Theory

Inverse Problem Theory is written for physicists, geophysicists and all scientists facing the problem of quantitative interpretation of experimental data. Although it contains a lot of mathematics, it is not intended as a mathematical book, but rather tries to explain how a method of acquisition of information can be applied to the actual world. The book provides a comprehensive, up-to-date description of the methods to be used for fitting experimental data, or to estimate model parameters, and to unify these

Inverse Problems in the Mathematical Sciences

Inverse Problems in the Mathematical Sciences
  • Author : Charles W. Groetsch
  • Publisher : Springer Science & Business Media
  • Release : 14 December 2013
GET THIS BOOKInverse Problems in the Mathematical Sciences

Inverse problems are immensely important in modern science and technology. However, the broad mathematical issues raised by inverse problems receive scant attention in the university curriculum. This book aims to remedy this state of affairs by supplying an accessible introduction, at a modest mathematical level, to the alluring field of inverse problems. Many models of inverse problems from science and engineering are dealt with and nearly a hundred exercises, of varying difficulty, involving mathematical analysis, numerical treatment, or modelling of

Numerical Methods for Inverse Problems

Numerical Methods for Inverse Problems
  • Author : Michel Kern
  • Publisher : John Wiley & Sons
  • Release : 07 June 2016
GET THIS BOOKNumerical Methods for Inverse Problems

This book studies methods to concretely address inverse problems. An inverse problem arises when the causes that produced a given effect must be determined or when one seeks to indirectly estimate the parameters of a physical system. The author uses practical examples to illustrate inverse problems in physical sciences. He presents the techniques and specific methods chosen to solve inverse problems in a general domain of application, choosing to focus on a small number of methods that can be used

Advanced Data Assimilation for Geosciences

Advanced Data Assimilation for Geosciences
  • Author : Eric Blayo
  • Publisher : Oxford University Press, USA
  • Release : 30 October 2014
GET THIS BOOKAdvanced Data Assimilation for Geosciences

In many applications of geophysics (weather forecast, study of climate evolution and variability), it is necessary to get the best possible estimate of the state of the system under study. In general, information about this system comes from observations and numerical models. However, none of these sources is perfect. Data assimilation designates the set of mathematical methods used to optimally combine observations with models, to fulfil the need of an accurateestimate of the system state. Because of the weather forecast

Geophysical Data Analysis: Understanding Inverse Problem Theory and Practice

Geophysical Data Analysis: Understanding Inverse Problem Theory and Practice
  • Author : Max A. Meju
  • Publisher : SEG Books
  • Release : 14 August 1994
GET THIS BOOKGeophysical Data Analysis: Understanding Inverse Problem Theory and Practice

This publication is designed to provide a practical understanding of methods of parameter estimation and uncertainty analysis. The practical problems covered range from simple processing of time- and space-series data to inversion of potential field, seismic, electrical, and electromagnetic data. The various formulations are reconciled with field data in the numerous examples provided in the book; well-documented computer programmes are also given to show how easy it is to implement inversion algorithms.

Inverse Problems with Applications in Science and Engineering

Inverse Problems with Applications in Science and Engineering
  • Author : Daniel Lesnic
  • Publisher : CRC Press
  • Release : 10 November 2021
GET THIS BOOKInverse Problems with Applications in Science and Engineering

Driven by the advancement of industrial mathematics and the need for impact case studies, Inverse Problems with Applications in Science and Engineering thoroughly examines the state-of-the-art of some representative classes of inverse and ill-posed problems for partial differential equations (PDEs). The natural practical applications of this examination arise in heat transfer, electrostatics, porous media, acoustics, fluid and solid mechanics – all of which are addressed in this text. Features: Covers all types of PDEs — namely, elliptic (Laplace’s, Helmholtz, modified Helmholtz,

An Introduction to Data Analysis and Uncertainty Quantification for Inverse Problems

An Introduction to Data Analysis and Uncertainty Quantification for Inverse Problems
  • Author : Luis Tenorio
  • Publisher : SIAM
  • Release : 06 July 2017
GET THIS BOOKAn Introduction to Data Analysis and Uncertainty Quantification for Inverse Problems

Inverse problems are found in many applications, such as medical imaging, engineering, astronomy, and geophysics, among others. To solve an inverse problem is to recover an object from noisy, usually indirect observations. Solutions to inverse problems are subject to many potential sources of error introduced by approximate mathematical models, regularization methods, numerical approximations for efficient computations, noisy data, and limitations in the number of observations; thus it is important to include an assessment of the uncertainties as part of the

Inverse Problems in Groundwater Modeling

Inverse Problems in Groundwater Modeling
  • Author : Ne-Zheng Sun
  • Publisher : Springer Science & Business Media
  • Release : 17 April 2013
GET THIS BOOKInverse Problems in Groundwater Modeling

... A diskette with the updated programme of Appendix C and examples is available through the author at a small fee. email: nezheng@ucla.edu fax: 1--310--825--5435 ... This book systematically discusses basic concepts, theory, solution methods and applications of inverse problems in groundwater modeling. It is the first book devoted to this subject. The inverse problem is defined and solved in both deterministic and statistic frameworks. Various direct and indirect methods are discussed and compared. As a useful tool,

Model Calibration and Parameter Estimation

Model Calibration and Parameter Estimation
  • Author : Ne-Zheng Sun,Alexander Sun
  • Publisher : Springer
  • Release : 01 July 2015
GET THIS BOOKModel Calibration and Parameter Estimation

This three-part book provides a comprehensive and systematic introduction to these challenging topics such as model calibration, parameter estimation, reliability assessment, and data collection design. 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