A novel sequential exploration-exploitation sampling strategy for global metamodeling

Jiang, Ping; Shu, Leshi; Zhou, Qi; Zhou, Hui; Shao, Xinyu; Xu, Junnan

doi:10.1016/j.ifacol.2015.12.183

Cited by 22 publications

(17 citation statements)

References 11 publications

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“…7b 2 . For instance, an error at any point x can be approximated as the superposition of the relative errors between the current metamodel and the leave-one-out metamodels considering successively the lack of each sample [45], as follows The method has also been adopted by Kim et al [50] and extended as a weighted version in Jiang et al [42].…”

Section: Continuousmentioning

confidence: 99%

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State-of-the-Art and Comparative Review of Adaptive Sampling Methods for Kriging

Fuhg¹,

Fau²,

Nackenhorst³

2020

Arch Computat Methods Eng

164

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Metamodels aim to approximate characteristics of functions or systems from the knowledge extracted on only a finite number of samples. In recent years kriging has emerged as a widely applied metamodeling technique for resource-intensive computational experiments. However its prediction quality is highly dependent on the size and distribution of the given training points. Hence, in order to build proficient kriging models with as few samples as possible adaptive sampling strategies have gained considerable attention. These techniques aim to find pertinent points in an iterative manner based on information extracted from the current metamodel. A review of adaptive schemes for kriging proposed in the literature is presented in this article. The objective is to provide the reader with an overview of the main principles of adaptive techniques, and insightful details to pertinently employ available tools depending on the application at hand. In this context commonly applied strategies are compared with regards to their characteristics and approximation capabilities. In light of these experiments, it is found that the success of a scheme depends on the features of a specific problem and the goal of the analysis. In order to facilitate the entry into adaptive sampling a guide is provided. All experiments described herein are replicable using a provided open source toolbox.

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Section: Continuousmentioning

confidence: 99%

“…proportional to the average minimum distance of all sample points. A similar approach designed by Jiang et al [42] defines the cluster threshold S Jiang as detailed in Box 1.…”

Section: Continuous Distance Criterionmentioning

confidence: 99%

State-of-the-Art and Comparative Review of Adaptive Sampling Methods for Kriging

Fuhg¹,

Fau²,

Nackenhorst³

2020

Arch Computat Methods Eng

164

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show abstract

“…The query-by-committee (QBC) strategy [ 31 , 35 , 36 ] uses the predictions of multiple competing surrogate models as a committee to predict the response at a candidate point. The cross validation (CV) based adaptive sampling [ 37 , 38 , 39 ] estimates the prediction error at a candidate point using a cross-validation process. Finally, The gradient-based adaptive sampling [ 40 , 41 , 42 ] uses the local gradient information of the model to represent the prediction errors.…”

Section: Related Workmentioning

confidence: 99%

“…The main limitation of the current adaptive sampling methods is that they seek to identify the points that most favor a predetermined meta-model based on its performance [ 37 , 38 , 39 , 40 , 41 , 42 ]. However, it is not easy to determine if the points were selected due to the target system behavior or to an intrinsic limitation of the selected meta-model.…”

Section: Related Workmentioning

confidence: 99%

ASAMS: An Adaptive Sequential Sampling and Automatic Model Selection for Artificial Intelligence Surrogate Modeling

Duchanoy

Calvo

Moreno-Armendáriz

2020

Sensors

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Surrogate Modeling (SM) is often used to reduce the computational burden of time-consuming system simulations. However, continuous advances in Artificial Intelligence (AI) and the spread of embedded sensors have led to the creation of Digital Twins (DT), Design Mining (DM), and Soft Sensors (SS). These methodologies represent a new challenge for the generation of surrogate models since they require the implementation of elaborated artificial intelligence algorithms and minimize the number of physical experiments measured. To reduce the assessment of a physical system, several existing adaptive sequential sampling methodologies have been developed; however, they are limited in most part to the Kriging models and Kriging-model-based Monte Carlo Simulation. In this paper, we integrate a distinct adaptive sampling methodology to an automated machine learning methodology (AutoML) to help in the process of model selection while minimizing the system evaluation and maximizing the system performance for surrogate models based on artificial intelligence algorithms. In each iteration, this framework uses a grid search algorithm to determine the best candidate models and perform a leave-one-out cross-validation to calculate the performance of each sampled point. A Voronoi diagram is applied to partition the sampling region into some local cells, and the Voronoi vertexes are considered as new candidate points. The performance of the sample points is used to estimate the accuracy of the model for a set of candidate points to select those that will improve more the model’s accuracy. Then, the number of candidate models is reduced. Finally, the performance of the framework is tested using two examples to demonstrate the applicability of the proposed method.

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“…Another sampling challenge touched on above is efficient sampling for updating surrogate models, balancing exploration (global search) and exploitation (precise solution) of the design space [33,34,35]. Previous studies aimed to achieve the following goals simultaneously: 1) finding a global solution, 2) finding an accurate local solution, and 3) limiting the number of high-fidelity simulations.…”

Section: Introductionmentioning

confidence: 99%

A Multiobjective Adaptive Surrogate Modeling-Based Optimization (MO-ASMO) Framework Using Efficient Sampling Strategies

Lee

Corman

Ewoldt

et al. 2017

Volume 2B: 43rd Design Automation Conference

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A novel multiobjective adaptive surrogate modeling-based optimization (MO-ASMO) framework is proposed to utilize a minimal number of training samples efficiently for sequential model updates. All the sample points are enforced to be feasible, and to provide coverage of sparsely explored sparse design regions using a new optimization subproblem. The MO-ASMO method only evaluates high-fidelity functions at feasible sample points. During an exploitation sample phase, samples are selected to enhance solution accuracy rather than the global exploration. Sampling tasks are especially challenging for multiobjective optimization; for an n-dimensional design space, a strategy is required for generating model update sample points near an (n − 1)-dimensional hypersurface corresponding to the Pareto set in the design space. This is addressed here using a force-directed layout algorithm, adapted from graph visualization strategies, to distribute feasible sample points evenly near the estimated Pareto set. Model validation samples are chosen uniformly on the Pareto set hypersurface, and surrogate model estimates at these points are compared to high-fidelity model responses. All high-fidelity model evaluations are stored for later use to train an updated surrogate model. The MO-ASMO algorithm, along with the set of new sampling strategies, are tested using two mathematical and one realistic engineering problems. The second mathematical test problems is specifically designed to test the limits of this algorithm to cope with very narrow, non-convex feasible domains. It involves oscillatory objective functions, giving rise to a discontinuous set of Pareto-optimal solutions. Also, the third test problem demonstrates that the MO-ASMO algorithm can handle a practical engineering problem with more than 10 design variables and black-box simulations. The efficiency of the MO-ASMO algorithm is demonstrated by comparing the result of two mathematical problems to the results of the NSGA-II algorithm in terms of the number of high fidelity function evaluations, and is shown to reduce total function evaluations by several orders of magnitude when converging to the same Pareto sets.

show abstract

A novel sequential exploration-exploitation sampling strategy for global metamodeling

Cited by 22 publications

References 11 publications

State-of-the-Art and Comparative Review of Adaptive Sampling Methods for Kriging

State-of-the-Art and Comparative Review of Adaptive Sampling Methods for Kriging

ASAMS: An Adaptive Sequential Sampling and Automatic Model Selection for Artificial Intelligence Surrogate Modeling

A Multiobjective Adaptive Surrogate Modeling-Based Optimization (MO-ASMO) Framework Using Efficient Sampling Strategies

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