Robust Surrogate Models for Uncertainty Quantification and Nuclear Engineering Applications PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Robust Surrogate Models for Uncertainty Quantification and Nuclear Engineering Applications PDF full book. Access full book title Robust Surrogate Models for Uncertainty Quantification and Nuclear Engineering Applications by B. Oparaji. Download full books in PDF and EPUB format.
Author: B. Oparaji
Publisher:
ISBN:
Category :
Languages : en
Pages :
Get Book Here
Book Description
Author: B. Oparaji
Publisher:
ISBN:
Category :
Languages : en
Pages :
Get Book Here
Book Description
Author: 伯歐吉
Publisher:
ISBN:
Category :
Languages : en
Pages :
Get Book Here
Book Description
Author: Flavio Canavero
Publisher: Springer
ISBN: 3030048705
Category : Technology & Engineering
Languages : en
Pages : 184
Get Book Here
Book Description
This book provides an overview of state-of-the-art uncertainty quantification (UQ) methodologies and applications, and covers a wide range of current research, future challenges and applications in various domains, such as aerospace and mechanical applications, structure health and seismic hazard, electromagnetic energy (its impact on systems and humans) and global environmental state change. Written by leading international experts from different fields, the book demonstrates the unifying property of UQ theme that can be profitably adopted to solve problems of different domains. The collection in one place of different methodologies for different applications has the great value of stimulating the cross-fertilization and alleviate the language barrier among areas sharing a common background of mathematical modeling for problem solution. The book is designed for researchers, professionals and graduate students interested in quantitatively assessing the effects of uncertainties in their fields of application. The contents build upon the workshop “Uncertainty Modeling for Engineering Applications” (UMEMA 2017), held in Torino, Italy in November 2017.
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 16
Get Book Here
Book Description
Author: Ralph C. Smith
Publisher: SIAM
ISBN: 1611973228
Category : Computers
Languages : en
Pages : 400
Get Book Here
Book Description
The field of uncertainty quantification is evolving rapidly because of increasing emphasis on models that require quantified uncertainties for large-scale applications, novel algorithm development, and new computational architectures that facilitate implementation of these algorithms. Uncertainty Quantification: Theory, Implementation, and Applications provides readers with the basic concepts, theory, and algorithms necessary to quantify input and response uncertainties for simulation models arising in a broad range of disciplines. The book begins with a detailed discussion of applications where uncertainty quantification is critical for both scientific understanding and policy. It then covers concepts from probability and statistics, parameter selection techniques, frequentist and Bayesian model calibration, propagation of uncertainties, quantification of model discrepancy, surrogate model construction, and local and global sensitivity analysis. The author maintains a complementary web page where readers can find data used in the exercises and other supplementary material.
Author: Yan Wang
Publisher: Elsevier
ISBN: 0443136629
Category : Technology & Engineering
Languages : en
Pages : 0
Get Book Here
Book Description
Fundamentals of Uncertainty Quantification for Engineers: Methods and Models provides a comprehensive introduction to uncertainty quantification (UQ) accompanied by a wide variety of applied examples, implementation details, and practical exercises to reinforce the concepts outlined in the book. Sections start with a review of the history of probability theory and recent developments of UQ methods in the domains of applied mathematics and data science. Major concepts of probability axioms, conditional probability, and Bayes’ rule are discussed and examples of probability distributions in parametric data analysis, reliability, risk analysis, and materials informatics are included. Random processes, sampling methods, and surrogate modeling techniques including multivariate polynomial regression, Gaussian process regression, multi-fidelity surrogate, support-vector machine, and decision tress are also covered. Methods for model selection, calibration, and validation are introduced next, followed by chapters on sensitivity analysis, stochastic expansion methods, Markov models, and non-probabilistic methods. The book concludes with a chapter describing the methods that can be used to predict UQ in systems, such as Monte Carlo, stochastic expansion, upscaling, Langevin dynamics, and inverse problems, with example applications in multiscale modeling, simulations, and materials design. Introduces all major topics of uncertainty quantification with engineering examples, implementation details, and practical exercises provided in all chapters Features examples from a wide variety of science and engineering disciplines (e.g. aerospace, mechanical, material, manufacturing, multiscale simulation) Discusses materials informatics, sampling methods, surrogate modeling techniques, decision tress, multivariate polynomial regression, and more
Author: Kathleen Lynn Schmidt
Publisher:
ISBN:
Category :
Languages : en
Pages : 92
Get Book Here
Book Description
Author: Komahan Boopathy
Publisher:
ISBN:
Category : Engineering design
Languages : en
Pages : 178
Get Book Here
Book Description
Surrogate models are widely used as approximations to exact functions that are computationally expensive to evaluate. The choice of model training information and the estimation of the accuracy of surrogate models are major research avenues. In this work, a unified dynamic framework for surrogate model training point selection and error estimation is proposed. Building auxiliary local surrogate models over sub-domains of the global surrogate model forms the basis of the framework. A discrepancy function, defined as the absolute difference between response predictions from global and local surrogate models for randomly chosen test candidates, drives the framework.The framework preferably evaluates the expensive exact function at locations, where the value of the discrepancy function is high and when a distance-constraint to previously existing training points are satisfied. As a result, the surrogate model is continually refined in regions of higher uncertainty in prediction, and a better spread of training points is also achieved. Unlike most training point selection approaches, the framework addresses surrogate training from two disparate contexts, as training in the presence and absence of derivative information. The local surrogate models use the derivative information when available and affect the framework via the discrepancy function, and helps determine the locations that require derivative information. The benefits of the dynamic training approach are demonstrated with analytical test functions and the construction of a two-dimensional aerodynamic database. The results show that the proposed method improves the convergence monotonicity and produces more accurate surrogate models, when compared to random and quasi-random training point selection strategies.The newly introduced discrepancy function is proposed as an approximation to the actual error in the prediction of the surrogate model leading to the quantities: root mean square discrepancy (RMSD) and maximum absolute discrepancy (MAD). The results demonstrate a close agreement of RMSD and MAE with the actual root mean square error (RMSE) and maximum absolute error (MAE), respectively. Therefore, RMSD and MAD are proposed as measures for the accuracy of the surrogate models in applications of practical interest. The benefit of surrogate validation comes without warranting any additional exact function evaluations, which makes the framework computationally viable. Multivariate interpolation and regression model is employed to build local surrogates, whereas the kriging and polynomial chaos expansions serve as global surrogate models. This demonstrates the applicability of the proposed framework to any surrogate model with an open choice of training data selection.Finally, the dynamically trained surrogate models are applied to uncertainty quantifications and optimizations under mixed epistemic and aleatory uncertainties (OUU), for structural and aerodynamic test cases. In the OUUs epistemic uncertainties are propagated via box-constrained optimizations, whereas the aleatory uncertainties are propagated via inexpensive sampling of the surrogate models. The structural test cases include designing a three-bar truss and a cantilever beam, whereas the aerodynamic test case involves the robust optimization (lift-constrained drag minimization) of an airfoil under steady flow conditions.
Author: Massimiliano Vasile
Publisher: Springer
ISBN: 9783030805449
Category : Technology & Engineering
Languages : en
Pages : 0
Get Book Here
Book Description
The 2020 International Conference on Uncertainty Quantification & Optimization gathered together internationally renowned researchers in the fields of optimization and uncertainty quantification. The resulting proceedings cover all related aspects of computational uncertainty management and optimization, with particular emphasis on aerospace engineering problems. The book contributions are organized under four major themes: Applications of Uncertainty in Aerospace & Engineering Imprecise Probability, Theory and Applications Robust and Reliability-Based Design Optimisation in Aerospace Engineering Uncertainty Quantification, Identification and Calibration in Aerospace Models This proceedings volume is useful across disciplines, as it brings the expertise of theoretical and application researchers together in a unified framework.
Author: Yan Wang
Publisher: Woodhead Publishing Limited
ISBN: 0081029411
Category : Materials science
Languages : en
Pages : 604
Get Book Here
Book Description
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.
