Bayesian inverse problems : fundamentals and engineering applications
Published
Boca Raton : CRC Press, 2021.
Status
Available Online
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Edition
First edition.
Language
English
ISBN
9781315232973, 1315232979, 9781351869652, 1351869655, 9781351869669, 1351869663, 9781351869645, 1351869647
Notes
Bibliography
Includes bibliographical references and index.
Description
"This book is intended to provide a bottom-up and fundamental understanding of the use of probabilistic methods and reliability analysis techniques in engineering applications. It covers from the fundamentals of the theory to real life applications in the field"--,Provided by publisher.
Biographical or Historical Data
Juan Chiach̕o-Ruano is an Associate Professor of Structural Engineering at University of Granada (Spain), and a researcher at the Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI). He has devoted his research career to the study and development of Bayesian methods in application to a wide range of Mechanical and Structural Engineering problems. Prior to joining University of Granada, he has developed a significant international research career working at top academic institutions in the UK and the USA. Manuel Chiach̕o-Ruano holds a PhD in Structural Engineering (2014) by the University of Granada (Spain). Currently, he is Associate Professor and Head of the Intelligent Prognostics and Cyber-physical Structural Systems Laboratory (iPHMLab) at the University of Granada. He has developed a significant part of his research in collaboration with the California Institute of Technology (USA), the University of Nottingham (UK) and NASA Ames Research Center (USA), during his stays at these institutions. Shankar Sankararaman received his PhD in Civil Engineering from Vanderbilt University, Nashville, TN, USA, in 2012. Soon after, he joined NASA Ames Research Center, where he developed Machine Learning algorithms and Bayesian methods for system health monitoring, prognostics, decision-making, and uncertainty management. Dr Sankararaman has co-authored a book on prognostics and published over 100 technical articles in international journals and conferences. Presently, Shankar is a scientist at Intuit AI, where he focuses on implementing cutting edge research in products and solutions for Intuit's customers.
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O'Reilly,O'Reilly Online Learning: Academic/Public Library Edition
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Citations
APA Citation, 7th Edition (style guide)
Chiachío-Ruano, J. (2021). Bayesian inverse problems: fundamentals and engineering applications (First edition.). CRC Press.
Chicago / Turabian - Author Date Citation, 17th Edition (style guide)Chiachío-Ruano, Juan, 1983-. 2021. Bayesian Inverse Problems: Fundamentals and Engineering Applications. Boca Raton: CRC Press.
Chicago / Turabian - Humanities (Notes and Bibliography) Citation, 17th Edition (style guide)Chiachío-Ruano, Juan, 1983-. Bayesian Inverse Problems: Fundamentals and Engineering Applications Boca Raton: CRC Press, 2021.
Harvard Citation (style guide)Chiachío-Ruano, J. (2021). Bayesian inverse problems: fundamentals and engineering applications. First edn. Boca Raton: CRC Press.
MLA Citation, 9th Edition (style guide)Chiachío-Ruano, Juan. Bayesian Inverse Problems: Fundamentals and Engineering Applications First edition., CRC Press, 2021.
Note! Citations contain only title, author, edition, publisher, and year published. Citations should be used as a guideline and should be double checked for accuracy. Citation formats are based on standards as of August 2021.
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Grouped Work ID
b8e672ee-4b4c-4c7f-5f3d-844307fde1f1-eng
Grouping Information
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| Full title | bayesian inverse problems fundamentals and engineering applications |
| Author | juan chiachío ruano university of granada spain ma |
| Grouping Category | book |
| Last Update | 2025-01-24 12:33:29PM |
| Last Indexed | 2025-02-07 03:26:44AM |
Book Cover Information
| Image Source | syndetics |
|---|---|
| First Loaded | Feb 5, 2025 |
| Last Used | Feb 5, 2025 |
Marc Record
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| 245 | 0 | 0 | |a Bayesian inverse problems :|b fundamentals and engineering applications /|c editors, Juan Chiachío-Ruano, University of Granada, Spain, Manuel Chiachío-Ruano, University of Granada, Spain, Shankar Sankararaman, Intuit Inc., USA. |
| 250 | |a First edition. | ||
| 264 | 1 | |a Boca Raton :|b CRC Press,|c 2021. | |
| 300 | |a 1 online resource | ||
| 336 | |a text|b txt|2 rdacontent | ||
| 337 | |a computer|b c|2 rdamedia | ||
| 338 | |a online resource|b cr|2 rdacarrier | ||
| 504 | |a Includes bibliographical references and index. | ||
| 505 | 0 | |a Cover -- Title Page -- Copyright Page -- Dedication -- Preface -- Table of Contents -- List of Figures -- List of Tables -- Contributors -- Part I Fundamentals -- 1. Introduction to Bayesian Inverse Problems -- 1.1 Introduction -- 1.2 Sources of uncertainty -- 1.3 Formal definition of probability -- 1.4 Interpretations of probability -- 1.4.1 Physical probability -- 1.4.2 Subjective probability -- 1.5 Probability fundamentals -- 1.5.1 Bayes' Theorem -- 1.5.2 Total probability theorem -- 1.6 The Bayesian approach to inverse problems -- 1.6.1 The forward problem -- 1.6.2 The inverse problem -- 1.7 Bayesian inference of model parameters -- 1.7.1 Markov Chain Monte Carlo methods -- 1.7.1.1 Metropolis-Hasting algorithm -- 1.8 Bayesian model class selection -- 1.8.1 Computation of the evidence of a model class -- 1.8.2 Information-theory approach to model-class selection -- 1.9 Concluding remarks -- 2. Solving Inverse Problems by Approximate Bayesian Computation -- 2.1 Introduction to the ABC method -- 2.2 Basis of ABC using Subset Simulation -- 2.2.1 Introduction to Subset Simulation -- 2.2.2 Subset Simulation for ABC -- 2.3 The ABC-SubSim algorithm -- 2.4 Summary -- 3. Fundamentals of Sequential System Monitoring and Prognostics Methods -- 3.1 Fundamentals -- 3.1.1 Prognostics and SHM -- 3.1.2 Damage response modelling -- 3.1.3 Interpreting uncertainty for prognostics -- 3.1.4 Prognostic performance metrics -- 3.2 Bayesian tracking methods -- 3.2.1 Linear Bayesian Processor: The Kalman Filter -- 3.2.2 Unscented Transformation and Sigma Points: The Unscented Kalman Filter -- 3.2.3 Sequential Monte Carlo methods: Particle Filters -- 3.2.3.1 Sequential importance sampling -- 3.2.3.2 Resampling -- 3.3 Calculation of EOL and RUL -- 3.3.1 The failure prognosis problem -- 3.3.2 Future state prediction -- 3.4 Summary. | |
| 505 | 8 | |a 7. Fast Bayesian Approach for Stochastic Model Updating using Modal Information from Multiple Setups -- 7.1 Introduction -- 7.2 Probabilistic consideration of frequency-domain responses -- 7.2.1 PDF of multivariate FFT coefficients -- 7.2.2 PDF of PSD matrix -- 7.2.3 PDF of the trace of the PSD matrix -- 7.3 A two-stage fast Bayesian operational modal analysis -- 7.3.1 Prediction error model connecting modal responses and measurements -- 7.3.2 Spectrum variables identification using FBSTA -- 7.3.3 Mode shape identification using FBSDA -- 7.3.4 Statistical modal information for model updating -- 7.4 Bayesian model updating with modal data from multiple setups -- 7.4.1 Structural model class -- 7.4.2 Formulation of Bayesian model updating -- 7.4.2.1 The introduction of instrumental variables system mode shapes -- 7.4.2.2 Probability model connecting 'system mode shapes' and measured local mode shape -- 7.4.2.3 Probability model for the eigenvalue equation errors -- 7.4.2.4 Negative log-likelihood function for model updating -- 7.4.3 Solution strategy -- 7.5 Numerical example -- 7.5.1 Robustness test of the probabilistic model of trace of PSD matrix -- 7.5.2 Bayesian operational modal analysis -- 7.5.3 Bayesian model updating -- 7.6 Experimental study -- 7.6.1 Bayesian operational modal analysis -- 7.6.2 Bayesian model updating -- 7.7 Concluding remarks -- 8. A Worked-out Example of Surrogate-based Bayesian Parameter and Field Identification Methods -- 8.1 Introduction -- 8.2 Numerical modelling of seabed displacement -- 8.2.1 The deterministic computation of seabed displacements -- 8.2.2 Modified probabilistic formulation -- 8.3 Surrogate modelling -- 8.3.1 Computation of the surrogate by orthogonal projection -- 8.3.2 Computation of statistics -- 8.3.3 Validating surrogate models -- 8.4 Efficient representation of random fields. | |
| 505 | 8 | |a 8.4.1 Karhunen-Loève Expansion (KLE) -- 8.4.2 Proper Orthogonal Decomposition (POD) -- 8.5 Identification of the compressibility field -- 8.5.1 Bayes' Theorem -- 8.5.2 Sampling-based procedures-the MCMC method -- 8.5.3 The Kalman filter and its modified versions -- 8.5.3.1 The Kalman filter -- 8.5.3.2 The ensemble Kalman filter -- 8.5.3.3 The PCE-based Kalman filter -- 8.5.4 Non-linear filters -- 8.6 Summary, conclusion, and outlook -- Appendices -- Appendix A: FEM computation of seabed displacements -- Appendix B: Hermite polynomials -- B.1 Generation of Hermite Polynomials -- B.2 Calculation of the norms -- B.3 Quadrature points and weights -- Appendix C: Galerkin solution of the Karhunen Loève eigenfunction problem -- Appendix D: Computation of the PCE Coefficients by Orthogonal projection -- Bibliography -- Index. | |
| 520 | |a "This book is intended to provide a bottom-up and fundamental understanding of the use of probabilistic methods and reliability analysis techniques in engineering applications. It covers from the fundamentals of the theory to real life applications in the field"--|c Provided by publisher. | ||
| 545 | 0 | |a Juan Chiach̕o-Ruano is an Associate Professor of Structural Engineering at University of Granada (Spain), and a researcher at the Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI). He has devoted his research career to the study and development of Bayesian methods in application to a wide range of Mechanical and Structural Engineering problems. Prior to joining University of Granada, he has developed a significant international research career working at top academic institutions in the UK and the USA. Manuel Chiach̕o-Ruano holds a PhD in Structural Engineering (2014) by the University of Granada (Spain). Currently, he is Associate Professor and Head of the Intelligent Prognostics and Cyber-physical Structural Systems Laboratory (iPHMLab) at the University of Granada. He has developed a significant part of his research in collaboration with the California Institute of Technology (USA), the University of Nottingham (UK) and NASA Ames Research Center (USA), during his stays at these institutions. Shankar Sankararaman received his PhD in Civil Engineering from Vanderbilt University, Nashville, TN, USA, in 2012. Soon after, he joined NASA Ames Research Center, where he developed Machine Learning algorithms and Bayesian methods for system health monitoring, prognostics, decision-making, and uncertainty management. Dr Sankararaman has co-authored a book on prognostics and published over 100 technical articles in international journals and conferences. Presently, Shankar is a scientist at Intuit AI, where he focuses on implementing cutting edge research in products and solutions for Intuit's customers. | |
| 588 | 0 | |a Print version record. | |
| 590 | |a O'Reilly|b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Inverse problems (Differential equations)|9 43471 | |
| 650 | 0 | |a Bayesian statistical decision theory.|9 30352 | |
| 700 | 1 | |a Chiachío-Ruano, Juan,|d 1983-|e editor.|1 https://id.oclc.org/worldcat/entity/E39PCjxwpfcBJqjXtWGkqKmXBd | |
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