Uncertainty in the surrogate models
High dimensional output surrogate models for uncertainty and sensitivity analyses
... optimisation, i.e., when thousands of repeated calls are necessary for di↵erent parameter/input choices. In sensitivity and uncertainty analysis, e.g., Monte Carlo sampling is used to extract statistics of an ...
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Robust Surrogate Models for Uncertainty Quantification and Nuclear Engineering Applications
... Consequently, surrogate-modelling (also called response surface and meta models) techniques are widely established in probabilistic ...settings. Surrogate mod- els approximate the expensive ...
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Considerations of Accuracy and Uncertainty with Kriging Surrogate Models in Single Objective Electromagnetic Design Optimization
... technique, surrogate modelling, with emphasis placed on the importance of considering both the accuracy of, and uncertainty in, the surrogate ...kriging surrogate models is ...initial ...
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Uncertainty quantification in the assessment of seismic failure probability of nonstructural components using surrogate models
... approach for which the excitation is defined at the surface tends to quasi-systematically underestimate the risk of failure. To avoid this and knowing that in the non-linear case, the deconvolution problem is ...
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Evaluation of unknown groundwater contaminant sources characterization efficiency under hydrogeologic uncertainty in an experimental aquifer site by utilizing surrogate models
... develop surrogate models that can approximate the complex flow and transport processes in a contaminated ...developed surrogate models is that unlike the previous methods, they can be applied ...
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Evaluation of Unknown Groundwater Contaminant Sources Characterization Efficiency under Hydrogeologic Uncertainty in an Experimental Aquifer Site by Utilizing Surrogate Models
... develop surrogate models that can approximate the complex flow and transport processes in a contaminated ...developed surrogate models is that unlike the previous methods, they can be applied ...
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A surrogate modelling approach based on nonlinear dimension reduction for uncertainty quantification in groundwater flow models
... Simonoff, J.S.: Smoothing Methods in Statistics. Springer, New York (1996) Smolyak, S.A.: Quadrature and interpolation formulas for tensor products of certain classes of functions. Dokl. Akad. Nauk SSSR 4, 240–243 (1963) ...
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Uncertainty handling in surrogate assisted optimisation of games
... best2009 performance was improved notably. No obvious patterns in scaling behaviour were detected. Unfortunately, there did not seem to be any obvious pattern of performances regarding the different function groups ...
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Uncertainty-Integrated Surrogate Modeling for Complex System Optimization
... Concurrent Surrogate Model Selection ...a surrogate estimated using PEMF is applied to also develop a novel model management approach for engineering opti- ...computational models (both physics-based ...
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Surrogate models for composed simulation models in energy systems
... appropriate surrogate models to represent the simulation models ...computer-based models, it can be assumed that samples can be easily generated and therefore one of the many available ...
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Uncertainty quantification of brake squeal Iistability via surrogate modelling
... Brake surrogate models 168 ...the uncertainty and variability of brake systems, it is possible to figure out what percentage of manufactured brakes will „fail‟ in terms of noise either at the end of ...
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Surrogate models for the prediction of the aerodynamic performance of exhaust systems
... Fig. 14. Influence of regression function, correlation function and nugget value on e LOO of the Kriging model for the axial force coefficient C F x for engine E2. presented in this work based on the exhaust system ...
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Uncertainty quantification of squeal instability via surrogate modelling
... for uncertainty analysis of brake squeal with a significant saving of computational ...brake models are typically composed of different components, whose degrees of freedom are in the order of ...the ...
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Surrogate-assisted Bayesian inversion for landscape and basin evolution models
... evolution models poses significant chal- lenges to the inference and optimization of unknown model ...and uncertainty quantification of unknown model ...and uncertainty quantification for the ...
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Efficient Computation and Uncertainty Analysis of Underwater Acoustic Propagation based on Kriging Surrogate Model
... strong uncertainty with the dynamic changes of ocean movement. Uncertainty of various ocean parameters, such as sound velocity, horizontal distance, vertical depth, angle and frequency, lead to the ...
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On Design Mining: Coevolution and Surrogate Models
... 6 Conclusions Design mining represents a methodology through which the capabilities of computational intelli- gence can be exploited directly in the physical world through 3D printing. Our previous pilot study [55] ...
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Surrogate models for aerodynamic performance prediction.
... A number of changes could be made to the models presented, including the transfer functions used, as well as the learning algorithms. These could either be changed for all networks to investigate the performance ...
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Towards Better Integration of Surrogate Models and Optimizers
... Another important question about measuring of optimization performance is what kind of optimal values can really be reached under the restriction of a strictly limited budget. This also extends to the question of ...
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Analysis of Reactor Simulations Using Surrogate Models.
... from the problems that have classically plagued surrogates. Namely, the existence of many local optima. The extension of surrogates to multiphysics computer codes is a worthwhile investigation. In many of the problems ...
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Proper Orthogonal Decomposition for Surrogate Models in Aerodynamics
... the surrogate model, only 5 control points are ...the surrogate model are the positive or negative variations of the point positions along the y-axis with respect to the base configuration, within a range ...
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