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Produktbild: Large-Scale Inverse Problems and Quantification of Uncertainty

Large-Scale Inverse Problems and Quantification of Uncertainty

183,99 €

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Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

15.11.2010

Herausgeber

Lorenz Biegler + weitere

Verlag

Wiley

Seitenzahl

400

Maße (L/B/H)

24,2/15,9/2,3 cm

Gewicht

753 g

Sprache

Englisch

ISBN

978-0-470-69743-6

Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

15.11.2010

Herausgeber

Verlag

Wiley

Seitenzahl

400

Maße (L/B/H)

24,2/15,9/2,3 cm

Gewicht

753 g

Sprache

Englisch

ISBN

978-0-470-69743-6

Herstelleradresse

Libri GmbH
Europaallee 1
36244 Bad Hersfeld
DE

Email: GPSR Kontakt

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  • Produktbild: Large-Scale Inverse Problems and Quantification of Uncertainty

  • 1 Introduction
    1.1 Introduction
    1.2 Statistical Methods
    1.3 Approximation Methods
    1.4 Kalman Filtering
    1.5 Optimization

    2 A Primer of Frequentist and Bayesian Inference in Inverse Problems
    2.1 Introduction
    2.2 Prior Information and Parameters: What do you know, and what do you want to know?
    2.3 Estimators: What can you do with what you measure?
    2.4 Performance of estimators: How well can you do?
    2.5 Frequentist performance of Bayes estimators for a BNM
    2.6 Summary
    Bibliography

    3 Subjective Knowledge or Objective Belief? An Oblique Look to Bayesian Methods
    3.1 Introduction
    3.2 Belief, information and probability
    3.3 Bayes' formula and updating probabilities
    3.4 Computed examples involving hypermodels
    3.5 Dynamic updating of beliefs
    3.6 Discussion
    Bibliography

    4 Bayesian and Geostatistical Approaches to Inverse Problems
    4.1 Introduction
    4.2 The Bayesian and Frequentist Approaches
    4.3 Prior Distribution
    4.4 A Geostatistical Approach
    4.5 Concluding
    Bibliography

    5 Using the Bayesian Framework to Combine Simulations and Physical Observations
    for Statistical Inference
    5.1 Introduction
    5.2 Bayesian Model Formulation 
    5.3 Application: Cosmic Microwave Background
    5.4 Discussion
    Bibliography

    6 Bayesian Partition Models for Subsurface Characterization
    6.1 Introduction
    6.2 Model equations and problem setting
    6.3 Approximation of the response surface using the Bayesian Partition Model and two-stage
    MCMC
    6.4 Numerical results
    6.5 Conclusions
    Bibliography

    7 Surrogate and reduced-order modeling: a comparison of approaches for large-scale
    statistical inverse problems
    7.1 Introduction
    7.2 Reducing the computational cost of solving statistical inverse problems
    7.3 General formulation
    7.4 Model reduction
    7.5 Stochastic spectral methods
    7.6 Illustrative example
    7.7 Conclusions
    Bibliography

    8 Reduced basis approximation and a posteriori error estimation for parametrized
    parabolic PDEs; Application to real-time Bayesian parameter estimation
    8.1 Introduction
    8.2 Linear Parabolic Equations
    8.3 Bayesian Parameter Estimation
    8.4 Concluding Remarks
    Bibliography

    9 Calibration and Uncertainty Analysis for Computer Simulations with Multivariate
    Output
    9.1 Introduction
    9.2 Gaussian Process Models
    9.3 Bayesian Model Calibration
    9.4 Case Study: Thermal Simulation of Decomposing Foam
    9.5 Conclusions
    Bibliography

    10 Bayesian Calibration of Expensive Multivariate Computer Experiments
    10.1 Calibration of computer experiments
    10.2 Principal component emulation 
    10.3 Multivariate calibration
    10.4 Summary
    Bibliography

    11 The Ensemble Kalman Filter and Related Filters
    11.1 Introduction
    11.2 Model Assumptions
    11.3 The Traditional Kalman Filter (KF)
    11.4 The Ensemble Kalman Filter (EnKF)
    11.5 The Randomized Maximum Likelihood Filter (RMLF)
    11.6 The Particle Filter (PF)
    11.7 Closing Remarks
    11.8 Appendix A: Properties of the EnKF Algorithm
    11.9 Appendix B: Properties of the RMLF Algorithm
    Bibliography

    12 Using the ensemble Kalman Filter for history matching and uncertainty quantification
    of complex reservoir models
    12.1 Introduction
    12.2 Formulation and solution of the inverse problem
    12.3 EnKF history matching workflow
    12.4 Field Case
    12.5 Conclusion
    Bibliography

    13 Optimal Experimental Design for the Large-Scale Nonlinear Ill-posed Problem of
    Impedance Imaging
    13.1 Introduction
    13.2 Impedance Tomography
    13.3 Optimal Experimental Design - Background
    13.4 Optimal Experimental Design for Nonlinear Ill-Posed Problems
    13.5 Optimization Framework
    13.6 Numerical Results
    13.7 Discussion and Conclusions
    Bibliography

    14 Solving Stochastic Inverse Problems: A Sparse Grid Collocation Approach
    14.1 Introduction
    14.2 Mathematical developments
    14.3 Numerical Examples
    14.4 Summary
    Bibliography

    15 Uncertainty analysis for seismic inverse problems: two practical examples
    15.1 Introduction
    15.2 Traveltime inversion for velocity determination.
    15.3 Prestack stratigraphic inversion
    15.4 Conclusions

    Bibliography
    16 Solution of inverse problems using discrete ODE adjoints
    16.1 Introduction
    16.2 Runge-Kutta Methods
    16.3 Adaptive Steps
    16.4 Linear Multistep Methods
    16.5 Numerical Results
    16.6 Application to Data Assimilation
    16.7 Conclusions
    Bibliography
    TBD