Comparative studies of metamodelling techniques under multiple modelling criteria

Jin, Ruichen; Chen, W.; Simpson, Timothy W.

doi:10.1007/s00158-001-0160-4

Cited by 1,405 publications

(517 citation statements)

References 27 publications

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“…These factors are the noise level and the allowed budget of evaluations (a similar classification can be found in [18]). For the tuning factors, we selected the ones which have been changed within different studies [2, 4], i. e., the proportion of observations for the initial DOE and the choice of the covariance kernel.…”

Section: Algorithmic Factorsmentioning

confidence: 99%

A benchmark of kriging-based infill criteria for noisy optimization

Picheny

Wagner

Ginsbourger³

2013

Struct Multidisc Optim

279

178

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Responses of many real-world problems can only be evaluated perturbed by noise. In order to make an efficient optimization of these problems possible, intelligent optimization strategies successfully coping with noisy evaluations are required. In this article, a comprehensive review of existing kriging-based methods for the optimization of noisy functions is provided. In summary, ten methods for choosing the sequential samples are described using a unified formalism. They are compared on analytical benchmark problems, whereby the usual assumption of homoscedastic Gaussian noise made in the underlying models is meet. Different problem configurations (noise level, maximum number of observations, initial number of observations) and setups (covariance functions, budget, initial sample size) are considered. It is found that the choices of the initial sample size and the covariance function are not critical. The choice of the method, however, can result in significant differences in the performance. In particular, the three most intuitive criteria are found as poor alternatives. Although no criterion is found consistently more efficient than the others, two specialized methods appear more robust on average.

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Section: Algorithmic Factorsmentioning

confidence: 99%

A benchmark of kriging-based infill criteria for noisy optimization

Picheny

Wagner

Ginsbourger³

2013

Struct Multidisc Optim

279

178

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“…Since the real model is assumed to be deterministic (i.e., simulations of the same input configuration lead to the same output), it is desirable that the meta-model predicts as well the exact output value in correspondence of the training configurations (i.e., those known with absolute certainty). In this respect, among the numerous methods available in the literature (Jin et al, 2001;Shan & Wang, 2010), we resort to Kriging (Kleijnen, 2009;Matheron, 1963), i.e., Gaussian process modeling. Actually, Kriging is capable of modeling local behaviors of the response function and of diversifying the levels of accuracy of the same model within different regions.…”

Section: Meta-modelingmentioning

confidence: 99%

Simulation-based exploration of high-dimensional system models for identifying unexpected events

Turati

Pedroni

Zio

2017

Reliability Engineering & System Safety

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. Simulation-based exploration of highdimensional system models for identifying unexpected events. Reliability Engineering and System Safety.http://dx.doi.org/10.1016/j.ress.2017.04.004Simulation-based exploration of high-dimensional system models for identifying unexpected events AbstractMathematical numerical models are increasingly employed to simulate system behavior and identify sequences of events or configurations of the system's design and operational parameters that can lead the system to extreme conditions (Critical Region, CR). However, when a numerical model is: i) computationally expensive, ii) high-dimensional, and iii) complex, these tasks become challenging.In this paper, we propose an adaptive framework for efficiently tackling this problem: i) a dimensionality reduction technique is employed for identifying the factors and variables that most affect the system behavior; ii) a meta-model is sequentially trained to replace the computationally expensive model with a computationally cheap one; iii) an adaptive exploration algorithm based on Markov Chain Monte Carlo is introduced for exploring the system state space using the meta-model; iv) clustering and other techniques for the visualization of high dimensional data (e.g., parallel coordinates plot) are employed to summarize the retrieved information.The method is employed to explore a power network model involving 20 inputs. The CRs are properly identified with a limited computational cost, compared to another exploration technique of literature (i.e., Latin Hypercube Sampling).

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“…According to [33,34] cross-validation error is a common choice to measure the accuracy of the metamodel.…”

Section: Accuracy Metricsmentioning

confidence: 99%

Damage tolerance optimization of composite stringer run-out under tensile load

Badalló

Trias

Lindgaard

2015

Composite Structures

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Please cite this article as: Badalló, P., Trias, D., Lindgaard, E., Damage tolerance optimization of composite stringer run-out under tensile load, Composite Structures (2015), doi: http://dx.doi.org/10. 1016/j.compstruct.2015.07.025 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. AbstractStringer run-outs are a common solution to achieve the necessary strength, stiffness and geometric requirements of some structural solutions. The mechanical behaviour and complexity of such design details requires careful and thorough studies to ensure the structural integrity of the structure. The influence of some geometric variables of the run-out in the interface of the set stringer-panel is crucial to avoid the onset and growth of delamination cracks. In this study, a damage tolerant design of a stringer run-out is achieved by a process of design optimization and surrogate modelling techniques. A parametric finite element model created with python was used to generate a number of different geometrical designs of the stringer run-out. The relevant information of these models was adjusted using Radial Basis Functions (RBF). Finally, the optimization problem was solved using Quasi-Newton method and Genetic Algorithms. In the solution process, the RBF were used to compute the objective function: ratio between the energy release rate and the critical energy release rate according to the Benzeggagh-Kenane mixed mode criterion. Some design guidelines to obtain a damage tolerant stringer-panel interface have been derived from the results.

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Comparative studies of metamodelling techniques under multiple modelling criteria

Cited by 1,405 publications

References 27 publications

A benchmark of kriging-based infill criteria for noisy optimization

A benchmark of kriging-based infill criteria for noisy optimization

Simulation-based exploration of high-dimensional system models for identifying unexpected events

Damage tolerance optimization of composite stringer run-out under tensile load

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