Quantification Of Uncertainty Improving Efficiency And Technology

Author : Marta D'Elia
Publisher : Springer Nature
ISBN 13 : 3030487210
Total Pages : 290 pages
Book Rating : 4.18/5 ( download)

DOWNLOAD NOW!

Download or read book Quantification of Uncertainty: Improving Efficiency and Technology written by Marta D'Elia and published by Springer Nature. This book was released on 2020-07-30 with total page 290 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book explores four guiding themes – reduced order modelling, high dimensional problems, efficient algorithms, and applications – by reviewing recent algorithmic and mathematical advances and the development of new research directions for uncertainty quantification in the context of partial differential equations with random inputs. Highlighting the most promising approaches for (near-) future improvements in the way uncertainty quantification problems in the partial differential equation setting are solved, and gathering contributions by leading international experts, the book’s content will impact the scientific, engineering, financial, economic, environmental, social, and commercial sectors.

Author : Francesco Montomoli
Publisher : Springer
ISBN 13 : 3319146815
Total Pages : 99 pages
Book Rating : 4.12/5 ( download)

DOWNLOAD NOW!

Download or read book Uncertainty Quantification in Computational Fluid Dynamics and Aircraft Engines written by Francesco Montomoli and published by Springer. This book was released on 2015-02-19 with total page 99 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces novel design techniques developed to increase the safety of aircraft engines. The authors demonstrate how the application of uncertainty methods can overcome problems in the accurate prediction of engine lift, caused by manufacturing error. This in turn ameliorates the difficulty of achieving required safety margins imposed by limits in current design and manufacturing methods. This text shows that even state-of-the-art computational fluid dynamics (CFD) are not able to predict the same performance measured in experiments; CFD methods assume idealised geometries but ideal geometries do not exist, cannot be manufactured and their performance differs from real-world ones. By applying geometrical variations of a few microns, the agreement with experiments improves dramatically, but unfortunately the manufacturing errors in engines or in experiments are unknown. In order to overcome this limitation, uncertainty quantification considers the probability density functions of manufacturing errors. It is then possible to predict the overall variation of the jet engine performance using stochastic techniques. Uncertainty Quantification in Computational Fluid Dynamics and Aircraft Engines demonstrates that some geometries are not affected by manufacturing errors, meaning that it is possible to design safer engines. Instead of trying to improve the manufacturing accuracy, uncertainty quantification when applied to CFD is able to indicate an improved design direction. This book will be of interest to gas turbine manufacturers and designers as well as CFD practitioners, specialists and researchers. Graduate and final year undergraduate students may also find it of use.

Author : Yan Wang
Publisher : Woodhead Publishing Limited
ISBN 13 : 0081029411
Total Pages : 604 pages
Book Rating : 4.11/5 ( download)

DOWNLOAD NOW!

Download or read book Uncertainty Quantification in Multiscale Materials Modeling written by Yan Wang and published by Woodhead Publishing Limited. This book was released on 2020-03-12 with total page 604 pages. Available in PDF, EPUB and Kindle. Book excerpt: Uncertainty Quantification in Multiscale Materials Modeling provides a complete overview of uncertainty quantification (UQ) in computational materials science. It provides practical tools and methods along with examples of their application to problems in materials modeling. UQ methods are applied to various multiscale models ranging from the nanoscale to macroscale. This book presents a thorough synthesis of the state-of-the-art in UQ methods for materials modeling, including Bayesian inference, surrogate modeling, random fields, interval analysis, and sensitivity analysis, providing insight into the unique characteristics of models framed at each scale, as well as common issues in modeling across scales.

Author : Andrew Dienstfrey
Publisher : Springer
ISBN 13 : 3642326773
Total Pages : 335 pages
Book Rating : 4.76/5 ( download)

DOWNLOAD NOW!

Download or read book Uncertainty Quantification in Scientific Computing written by Andrew Dienstfrey and published by Springer. This book was released on 2012-08-11 with total page 335 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed post-proceedings of the 10th IFIP WG 2.5 Working Conference on Uncertainty Quantification in Scientific Computing, WoCoUQ 2011, held in Boulder, CO, USA, in August 2011. The 24 revised papers were carefully reviewed and selected from numerous submissions. They are organized in the following topical sections: UQ need: risk, policy, and decision making, UQ theory, UQ tools, UQ practice, and hot topics. The papers are followed by the records of the discussions between the participants and the speaker.

Author : T. J. Sullivan
Publisher :
ISBN 13 : 9783919233943
Total Pages : 342 pages
Book Rating : 4.48/5 ( download)

DOWNLOAD NOW!

Download or read book Introduction to uncertainty quantification written by T. J. Sullivan and published by . This book was released on 2015 with total page 342 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Author : Roger Ghanem
Publisher : Springer
ISBN 13 : 9783319123844
Total Pages : 0 pages
Book Rating : 4.4X/5 ( download)

DOWNLOAD NOW!

Download or read book Handbook of Uncertainty Quantification written by Roger Ghanem and published by Springer. This book was released on 2016-05-08 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The topic of Uncertainty Quantification (UQ) has witnessed massive developments in response to the promise of achieving risk mitigation through scientific prediction. It has led to the integration of ideas from mathematics, statistics and engineering being used to lend credence to predictive assessments of risk but also to design actions (by engineers, scientists and investors) that are consistent with risk aversion. The objective of this Handbook is to facilitate the dissemination of the forefront of UQ ideas to their audiences. We recognize that these audiences are varied, with interests ranging from theory to application, and from research to development and even execution.

Author : Gianluigi Rozza
Publisher : Springer Nature
ISBN 13 : 3031550609
Total Pages : 265 pages
Book Rating : 4.07/5 ( download)

DOWNLOAD NOW!

Download or read book Reduction, Approximation, Machine Learning, Surrogates, Emulators and Simulators written by Gianluigi Rozza and published by Springer Nature. This book was released on with total page 265 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Author : Mohammad Ghavamzadeh
Publisher :
ISBN 13 : 9781680830880
Total Pages : 146 pages
Book Rating : 4.80/5 ( download)

DOWNLOAD NOW!

Download or read book Bayesian Reinforcement Learning written by Mohammad Ghavamzadeh and published by . This book was released on 2015-11-18 with total page 146 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian methods for machine learning have been widely investigated, yielding principled methods for incorporating prior information into inference algorithms. This monograph provides the reader with an in-depth review of the role of Bayesian methods for the reinforcement learning (RL) paradigm. The major incentives for incorporating Bayesian reasoning in RL are that it provides an elegant approach to action-selection (exploration/exploitation) as a function of the uncertainty in learning, and it provides a machinery to incorporate prior knowledge into the algorithms. Bayesian Reinforcement Learning: A Survey first discusses models and methods for Bayesian inference in the simple single-step Bandit model. It then reviews the extensive recent literature on Bayesian methods for model-based RL, where prior information can be expressed on the parameters of the Markov model. It also presents Bayesian methods for model-free RL, where priors are expressed over the value function or policy class. Bayesian Reinforcement Learning: A Survey is a comprehensive reference for students and researchers with an interest in Bayesian RL algorithms and their theoretical and empirical properties.

Author : Francis X. Giraldo
Publisher : Springer Nature
ISBN 13 : 3030550699
Total Pages : 559 pages
Book Rating : 4.91/5 ( download)

DOWNLOAD NOW!

Download or read book An Introduction to Element-Based Galerkin Methods on Tensor-Product Bases written by Francis X. Giraldo and published by Springer Nature. This book was released on 2020-10-30 with total page 559 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces the reader to solving partial differential equations (PDEs) numerically using element-based Galerkin methods. Although it draws on a solid theoretical foundation (e.g. the theory of interpolation, numerical integration, and function spaces), the book’s main focus is on how to build the method, what the resulting matrices look like, and how to write algorithms for coding Galerkin methods. In addition, the spotlight is on tensor-product bases, which means that only line elements (in one dimension), quadrilateral elements (in two dimensions), and cubes (in three dimensions) are considered. The types of Galerkin methods covered are: continuous Galerkin methods (i.e., finite/spectral elements), discontinuous Galerkin methods, and hybridized discontinuous Galerkin methods using both nodal and modal basis functions. In addition, examples are included (which can also serve as student projects) for solving hyperbolic and elliptic partial differential equations, including both scalar PDEs and systems of equations.

Author : Oliver Sander
Publisher : Springer Nature
ISBN 13 : 3030597024
Total Pages : 616 pages
Book Rating : 4.23/5 ( download)

DOWNLOAD NOW!

Download or read book DUNE — The Distributed and Unified Numerics Environment written by Oliver Sander and published by Springer Nature. This book was released on 2020-12-07 with total page 616 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Distributed and Unified Numerics Environment (Dune) is a set of open-source C++ libraries for the implementation of finite element and finite volume methods. Over the last 15 years it has become one of the most commonly used libraries for the implementation of new, efficient simulation methods in science and engineering. Describing the main Dune libraries in detail, this book covers access to core features like grids, shape functions, and linear algebra, but also higher-level topics like function space bases and assemblers. It includes extensive information on programmer interfaces, together with a wealth of completed examples that illustrate how these interfaces are used in practice. After having read the book, readers will be prepared to write their own advanced finite element simulators, tapping the power of Dune to do so.