Large-Scale Inverse Problems and Quantification of Uncertainty Ebook Tooltip Ebooks kunnen worden gelezen op uw computer en op daarvoor geschikte e-readers.
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Auteur:
L Biegler
- Engels
- E-book
- 9781119957584
- 24 juni 2011
- 388 pagina's
- Adobe ePub
Samenvatting
This book focuses on computational methods for large-scale statistical inverse problems and provides an introduction to statistical Bayesian and frequentist methodologies. Recent research advances for approximation methods are discussed, along with Kalman filtering methods and optimization-based approaches to solving inverse problems. The aim is to cross-fertilize the perspectives of researchers in the areas of data assimilation, statistics, large-scale optimization, applied and computational mathematics, high performance computing, and cutting-edge applications.
The solution to large-scale inverse problems critically depends on methods to reduce computational cost. Recent research approaches tackle this challenge in a variety of different ways. Many of the computational frameworks highlighted in this book build upon state-of-the-art methods for simulation of the forward problem, such as, fast Partial Differential Equation (PDE) solvers, reduced-order models and emulators of the forward problem, stochastic spectral approximations, and ensemble-based approximations, as well as exploiting the machinery for large-scale deterministic optimization through adjoint and other sensitivity analysis methods.
Key Features:
• Brings together the perspectives of researchers in areas of inverse problems and data assimilation.
• Assesses the current state-of-the-art and identify needs and opportunities for future research.
• Focuses on the computational methods used to analyze and simulate inverse problems.
• Written by leading experts of inverse problems and uncertainty quantification.
Graduate students and researchers working in statistics, mathematics and engineering will benefit from this book.
The solution to large-scale inverse problems critically depends on methods to reduce computational cost. Recent research approaches tackle this challenge in a variety of different ways. Many of the computational frameworks highlighted in this book build upon state-of-the-art methods for simulation of the forward problem, such as, fast Partial Differential Equation (PDE) solvers, reduced-order models and emulators of the forward problem, stochastic spectral approximations, and ensemble-based approximations, as well as exploiting the machinery for large-scale deterministic optimization through adjoint and other sensitivity analysis methods.
Key Features:
• Brings together the perspectives of researchers in areas of inverse problems and data assimilation.
• Assesses the current state-of-the-art and identify needs and opportunities for future research.
• Focuses on the computational methods used to analyze and simulate inverse problems.
• Written by leading experts of inverse problems and uncertainty quantification.
Graduate students and researchers working in statistics, mathematics and engineering will benefit from this book.
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Inhoud
- Taal
- en
- Bindwijze
- E-book
- Oorspronkelijke releasedatum
- 24 juni 2011
- Aantal pagina's
- 388
- Ebook Formaat
- Adobe ePub
- Illustraties
- Nee
Betrokkenen
- Hoofdauteur
- L Biegler
- Hoofdredacteur
- Lorenz Biegler
- Tweede Redacteur
- George Biros
- Co Redacteur
- Omar Ghattas
- Hoofduitgeverij
- Wiley
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- 1
- Extra groot lettertype
- Nee
- Studieboek
- Nee
EAN
- EAN
- 9781119957584
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