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Author: Daniela Steffes-lai
Publisher: Logos Verlag Berlin GmbH
ISBN: 3832536965
Category : Mathematics
Languages : en
Pages : 232
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Book Description
This work addresses the analysis of a sequential chain of processing steps, which is particularly important for the manufacture of robust product components. In each processing step, the material properties may have changed and distributions of related characteristics, for example, strains, may become inhomogeneous. For this reason, the history of the process including design-parameter uncertainties becomes relevant for subsequent processing steps. Therefore, we have developed a methodology, called PRO-CHAIN, which enables an efficient analysis, quantification, and propagation of uncertainties for complex process chains locally on the entire mesh. This innovative methodology has the objective to improve the overall forecast quality, specifically, in local regions of interest, while minimizing the computational effort of subsequent analysis steps. We have demonstrated the benefits and efficiency of the methodology proposed by means of real applications from the automotive industry.
Author: Daniela Steffes-lai
Publisher: Logos Verlag Berlin GmbH
ISBN: 3832536965
Category : Mathematics
Languages : en
Pages : 232
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Book Description
This work addresses the analysis of a sequential chain of processing steps, which is particularly important for the manufacture of robust product components. In each processing step, the material properties may have changed and distributions of related characteristics, for example, strains, may become inhomogeneous. For this reason, the history of the process including design-parameter uncertainties becomes relevant for subsequent processing steps. Therefore, we have developed a methodology, called PRO-CHAIN, which enables an efficient analysis, quantification, and propagation of uncertainties for complex process chains locally on the entire mesh. This innovative methodology has the objective to improve the overall forecast quality, specifically, in local regions of interest, while minimizing the computational effort of subsequent analysis steps. We have demonstrated the benefits and efficiency of the methodology proposed by means of real applications from the automotive industry.
Author: Stefan Peter Müller
Publisher: Logos Verlag Berlin GmbH
ISBN: 3832554440
Category : Mathematics
Languages : en
Pages : 232
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Book Description
Big data thrives on extracting knowledge from a large number of data sets. But how is an application possible when a single data set is several gigabytes in size? The innovative data compression techniques from the field of machine learning and modeling using Bayesian networks, which have been theoretically developed and practically implemented here, can reduce these huge amounts of data to a manageable size. By eliminating redundancies in location, time, and between simulation results, data reductions to less than 1% of the original size are possible. The developed method represents a promising approach whose use goes far beyond the application example of crash test simulations chosen here.
Author: Igor Farkaš
Publisher: Springer Nature
ISBN: 3030616096
Category : Computers
Languages : en
Pages : 891
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Book Description
The proceedings set LNCS 12396 and 12397 constitute the proceedings of the 29th International Conference on Artificial Neural Networks, ICANN 2020, held in Bratislava, Slovakia, in September 2020.* The total of 139 full papers presented in these proceedings was carefully reviewed and selected from 249 submissions. They were organized in 2 volumes focusing on topics such as adversarial machine learning, bioinformatics and biosignal analysis, cognitive models, neural network theory and information theoretic learning, and robotics and neural models of perception and action. *The conference was postponed to 2021 due to the COVID-19 pandemic.
Author: Emmanuel Laporte
Publisher: Springer Science & Business Media
ISBN: 1461200695
Category : Technology & Engineering
Languages : en
Pages : 202
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Book Description
Sensitivity analysis and optimal shape design are key issues in engineering that have been affected by advances in numerical tools currently available. This book, and its supplementary online files, presents basic optimization techniques that can be used to compute the sensitivity of a given design to local change, or to improve its performance by local optimization of these data. The relevance and scope of these techniques have improved dramatically in recent years because of progress in discretization strategies, optimization algorithms, automatic differentiation, software availability, and the power of personal computers. Numerical Methods in Sensitivity Analysis and Shape Optimization will be of interest to graduate students involved in mathematical modeling and simulation, as well as engineers and researchers in applied mathematics looking for an up-to-date introduction to optimization techniques, sensitivity analysis, and optimal design.
Author: Gary Tang
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Surrogates are used to mitigate the aggregate cost of simulation needed to perform a comprehensive uncertainty quantification (UQ) analysis. A realistic uncertainty analysis of any engineering system involves a large number of uncertainties, and as a result, the surrogates take inputs in a high dimensional space. We investigate surrogates that take the form of a truncated Legendre polynomial series, from which the coefficients associated to each polynomial basis function must be estimated. High dimensional estimation is a known instance of the curse of dimensionality, and for sufficiently "complex'" functions, an unsolved problem. In order to break the curse, we assume the function to be approximated is sparse in the Legendre polynomials and employ the machinery of l-1-regularized regression. We make three contributions under this theme. Firstly, we present a novel approach to choosing sample (design) points and show that it yields lower estimation error over a broad range of functions compared to existing sampling approaches. Secondly, we give a novel sparse estimator that effectively uses (partial) derivative information for estimation and show empirically that estimation using derivatives can be more efficient than function values if the derivatives are sparser than the function. Thirdly, we show that by exploiting the best k-term approximation} property of l-1-methods, we can quickly identify the most signfiicant uncertainties and reduce the dimensionality of the input space accordingly. We conclude by demonstrating the efficacy of these methods in a UQ analysis of a notional vertical axis wind turbine design.
Author: Dan Gabriel Cacuci
Publisher: Springer
ISBN: 9783031196348
Category : Science
Languages : en
Pages : 0
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Book Description
This text describes a comprehensive adjoint sensitivity analysis methodology (nth-CASAM), developed by the author, which enablesthe efficient and exact computation of arbitrarily high-order functional derivatives of model responses to model parameters in large-scale systems. The nth-CASAM framework is set in linearly increasing Hilbert spaces, each of state-function-dimensionality, as opposed to exponentially increasing parameter-dimensional spaces, thereby overcoming the so-called “curse of dimensionality” in sensitivity and uncertainty analysis. The nth-CASAM is applicable to any model; the larger the number of model parameters, the more efficient the nth-CASAM becomes for computing arbitrarily high-order response sensitivities. The book will be helpful to those working in the fields of sensitivity analysis, uncertainty quantification, model validation, optimization, data assimilation, model calibration, sensor fusion, reduced-order modelling, inverse problems and predictive modelling. This Volume Two, the second of three, presents the large-scale application of the nth-CASAM to perform a representative fourth-order sensitivity analysis of the Polyethylene-Reflected Plutonium benchmark described in the Nuclear Energy Agency (NEA) International Criticality Safety Benchmark Evaluation Project (ICSBEP) Handbook. This benchmark is modeled mathematically by the Boltzmann particle transport equation, involving 21,976 imprecisely-known parameters, the numerical solution of which requires representative large-scale computations. The sensitivity analysis presented in this volume is the most comprehensive ever performed in the field of reactor physics and the results presented in this book prove, perhaps counter-intuitively, that many of the 4th-order sensitivities are much larger than the corresponding 3rd-order ones, which are, in turn, much larger than the 2nd-order ones, all of which are much larger than the 1st-order sensitivities. Currently, the nth-CASAM is the only known methodology which enables such large-scale computations of exactly obtained expressions of arbitrarily-high-order response sensitivities.
Author: Hal Caswell
Publisher: Springer
ISBN: 3030105342
Category : Social Science
Languages : en
Pages : 308
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Book Description
This open access book shows how to use sensitivity analysis in demography. It presents new methods for individuals, cohorts, and populations, with applications to humans, other animals, and plants. The analyses are based on matrix formulations of age-classified, stage-classified, and multistate population models. Methods are presented for linear and nonlinear, deterministic and stochastic, and time-invariant and time-varying cases. Readers will discover results on the sensitivity of statistics of longevity, life disparity, occupancy times, the net reproductive rate, and statistics of Markov chain models in demography. They will also see applications of sensitivity analysis to population growth rates, stable population structures, reproductive value, equilibria under immigration and nonlinearity, and population cycles. Individual stochasticity is a theme throughout, with a focus that goes beyond expected values to include variances in demographic outcomes. The calculations are easily and accurately implemented in matrix-oriented programming languages such as Matlab or R. Sensitivity analysis will help readers create models to predict the effect of future changes, to evaluate policy effects, and to identify possible evolutionary responses to the environment. Complete with many examples of the application, the book will be of interest to researchers and graduate students in human demography and population biology. The material will also appeal to those in mathematical biology and applied mathematics.
Author: Reza N. Jazar
Publisher:
ISBN: 9781071604793
Category : Approximation theory
Languages : en
Pages :
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Book Description
Approximation Methods in Engineering and Science covers fundamental and advanced topics in three areas: Dimensional Analysis, Continued Fractions, and Stability Analysis of the Mathieu Differential Equation. Throughout the book, a strong emphasis is given to concepts and methods used in everyday calculations. Dimensional analysis is a crucial need for every engineer and scientist to be able to do experiments on scaled models and use the results in real world applications. Knowing that most nonlinear equations have no analytic solution, the power series solution is assumed to be the first approach to derive an approximate solution. However, this book will show the advantages of continued fractions and provides a systematic method to develop better approximate solutions in continued fractions. It also shows the importance of determining stability chart of the Mathieu equation and reviews and compares several approximate methods for that. The book provides the energy-rate method to study the stability of parametric differential equations that generates much better approximate solutions. Covers practical model-prototype analysis and nondimensionalization of differential equations; Coverage includes approximate methods of responses of nonlinear differential equations; Discusses how to apply approximation methods to analysis, design, optimization, and control problems; Discusses how to implement approximation methods to new aspects of engineering and physics including nonlinear vibration and vehicle dynamics
Author:
Publisher:
ISBN:
Category : Aeronautics
Languages : en
Pages : 1572
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Book Description
Author: Andrea Saltelli
Publisher: John Wiley & Sons
ISBN: 047087094X
Category : Mathematics
Languages : en
Pages : 232
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Book Description
Sensitivity analysis should be considered a pre-requisite for statistical model building in any scientific discipline where modelling takes place. For a non-expert, choosing the method of analysis for their model is complex, and depends on a number of factors. This book guides the non-expert through their problem in order to enable them to choose and apply the most appropriate method. It offers a review of the state-of-the-art in sensitivity analysis, and is suitable for a wide range of practitioners. It is focussed on the use of SIMLAB – a widely distributed freely-available sensitivity analysis software package developed by the authors – for solving problems in sensitivity analysis of statistical models. Other key features: Provides an accessible overview of the current most widely used methods for sensitivity analysis. Opens with a detailed worked example to explain the motivation behind the book. Includes a range of examples to help illustrate the concepts discussed. Focuses on implementation of the methods in the software SIMLAB - a freely-available sensitivity analysis software package developed by the authors. Contains a large number of references to sources for further reading. Authored by the leading authorities on sensitivity analysis.
