Adaptive design of experiments for global Kriging metamodeling through cross-validation information

Kyprioti, Aikaterini P.; Zhang, Jize; Taflanidis, Alexandros A.

doi:10.1007/s00158-020-02543-1

Cited by 23 publications

(16 citation statements)

References 36 publications

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“…Consequently, a local NIROM that can flexibly control the degree of overlap of each cluster was successfully developed. To further improve the performance of the model, a variance‐based adaptive sampling technique 35 was applied, which allowed both global exploration and local exploitation.…”

Section: Discussionmentioning

confidence: 99%

“…In the original approach, 35 global exploration was conducted based on the predictive variance of Kriging model, which is already defined in Equations ( 9) and (10). However, as there are multiple NIROMs in the local NIROM, the predictive variance of each model should be properly combined to calculate the variance of a global model.…”

Section: Variance-based Adaptive Sampling Methodsmentioning

confidence: 99%

“…Effective adaptive sampling can be achieved by appropriately adjusting the weight between these two criteria 34 . Although the sampling criteria in the previous study were originally designed for scalar quantity, 35 the present study proposed a simple modification to extend the criteria to local NIROM.…”

Section: Variance‐based Adaptive Sampling Methodsmentioning

confidence: 99%

“…We employed multinomial logistic regression (MLR) 28 as a classifier that can estimate the posterior probability of multiclasses. Furthermore, the present method applied the variance-based adaptive sampling technique 34,35 that considers both local exploitation and global exploration. As this resampling technique was originally designed for scalar quantities, 35 the current study presented a simple modification to extend it to local NIROM.…”

Section: Introductionmentioning

confidence: 99%

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Local non‐intrusive reduced order modeling based on soft clustering and classification algorithm

Kang

Shon

Yee

2022

Numerical Meth Engineering

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The use of non-intrusive reduced order modeling (NIROM) to approximate high-fidelity computer models has been steadily increased over the past decade. Recently, local NIROM has been proposed to improve the model accuracy in highly nonlinear problems in which distinct characteristic regimes coexist. The core concept of local NIROM is the decomposition of the parameter domains into a subregime to create multiple models. However, the existing local NIROM not only partitions the individual models in a mutually exclusive manner, but also uses a single model for prediction. This results in the extrapolation of surrogate models and the generation of artificial discontinuities. To mitigate these problems, a local NIROM that allows flexible overlapping of individual NIROMs is developed. This method softly partitions and combines individual NIROMs using machine learning techniques, such as fuzzy c-means and multinomial logistic regression. Furthermore, a variance-based adaptive sampling technique that can consider both local exploitation and global exploration is applied to improve model accuracy. The proposed method is validated against the transonic flow and in-flight icing problem, and demonstrates superior performance relative to its local counterpart by up to 16.5% and 33.9% in terms of normalized root-mean-square error and exclusive OR error, respectively.

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Section: Discussionmentioning

confidence: 99%

Section: Variance-based Adaptive Sampling Methodsmentioning

confidence: 99%

Section: Variance‐based Adaptive Sampling Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Local non‐intrusive reduced order modeling based on soft clustering and classification algorithm

Kang

Shon

Yee

2022

Numerical Meth Engineering

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“…Cross-validation [28] is often used to evaluate the generalization ability and reliability of a model or algorithm. In the related fields of machine learning, its basic idea is to group the original training data, one of which is used as a training set and the other as a verification set or a test set, train the corresponding model by using the training set, and verify the model by using the test set as an index to evaluate the learning algorithm.…”

Section: Merge Algorithm Of Cross-validationmentioning

confidence: 99%

Research on Parallel Support Vector Machine Based on Spark Big Data Platform

Yao

2021

Scientific Programming

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With the development of cloud computing and distributed cluster technology, the concept of big data has been expanded and extended in terms of capacity and value, and machine learning technology has also received unprecedented attention in recent years. Traditional machine learning algorithms cannot solve the problem of effective parallelization, so a parallelization support vector machine based on Spark big data platform is proposed. Firstly, the big data platform is designed with Lambda architecture, which is divided into three layers: Batch Layer, Serving Layer, and Speed Layer. Secondly, in order to improve the training efficiency of support vector machines on large-scale data, when merging two support vector machines, the “special points” other than support vectors are considered, that is, the points where the nonsupport vectors in one subset violate the training results of the other subset, and a cross-validation merging algorithm is proposed. Then, a parallelized support vector machine based on cross-validation is proposed, and the parallelization process of the support vector machine is realized on the Spark platform. Finally, experiments on different datasets verify the effectiveness and stability of the proposed method. Experimental results show that the proposed parallelized support vector machine has outstanding performance in speed-up ratio, training time, and prediction accuracy.

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Addressing the different sources of excitation variability in seismic response distribution estimation using kriging metamodeling

Kyprioti

Taflanidis

2022

Earthq Engng Struct Dyn

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This paper investigates the impact of the different sources of excitation variability within the stochastic kriging framework recently developed by the authors for the estimation of the distribution of engineering demand parameters (EDPs) in applications that the seismic hazard is described through stochastic ground motion models. For a given seismic event, described by seismicity characteristics such as the moment magnitude and the distance to source, one can distinguish two type of uncertainties in the excitation description: (g.i) the stochastic sequence utilized within the excitation model; (g.ii) the predictive models relating the ground motion parameters, such as significant duration, arias intensity, or frequency properties of seismic waves, to the seismicity characteristics. The original formulation of the authors treats (g.i) as aleatoric uncertainty and (g.ii) as parametric uncertainty, including the latter in the model parameters for the risk characterization, representing the metamodel input. Implementation establishes the EDP distribution approximation by utilizing a database of EDP estimates for a set of different input parameters, considering replications of these estimates for different stochastic sequences for some small part of this database. The database with replications is first leveraged to approximate the heteroscedas-2466

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Adaptive design of experiments for global Kriging metamodeling through cross-validation information

Cited by 23 publications

References 36 publications

Local non‐intrusive reduced order modeling based on soft clustering and classification algorithm

Local non‐intrusive reduced order modeling based on soft clustering and classification algorithm

Research on Parallel Support Vector Machine Based on Spark Big Data Platform

Addressing the different sources of excitation variability in seismic response distribution estimation using kriging metamodeling

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