A model-independent adaptive sequential sampling technique based on response nonlinearity estimation

Garbo, Andrea; German, Brian

doi:10.1007/s00158-019-02404-6

Cited by 8 publications

(5 citation statements)

References 41 publications

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“…Without the clustering penalty, the error-maximization sampling stopped after five iterations corresponding to 150 sampled points, an increase of 30 sampling points compared to the case with clustering penalty. The resulting surrogate model has an error in the same range although the MRAE is reduced for the surrogate model of reaction (16) while the RRMSE is increased for both reactions. One potential reasoning for this behavior can be seen in the simultaneous sampling approach.…”

Section: Surrogate Model Developmentmentioning

confidence: 98%

“…One potential reasoning for this behavior can be seen in the simultaneous sampling approach. The error-maximization sampling will focus on the surrogate model for reaction (16), as the MRAE is worse. Correspondingly, fewer points are sampled in the region in which the surrogate model of reaction ( 20) is worse, which resulted in an increase in the MRAE.…”

Section: Surrogate Model Developmentmentioning

confidence: 99%

“…A refinement metric based on local linear models was used together with a Pareto-rankingbased criterion to achieve a refinement-exploration balance of the surrogate model. 16 In general, adaptive sequential sampling routines often improve overall surrogate-model performance compared to single-shot static approaches and reduce the required number of samples. 14 In addition to the exploration and exploitation metrics used to improve the sampling routines in surrogate-model generation, constraints on the sampling domain are frequently used to effectively reduce the search space.…”

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confidence: 99%

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Constrained adaptive sampling for domain reduction in surrogate model generation: Applications to hydrogen production

et al. 2021

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We propose a new approach for sampling domain reduction for efficient surrogate model generation. Currently, the standard procedure is to use box constraints for the independent variables when sampling the exact simulator. However, by including additional inequality constraints to account for interdependencies between these variables, we can drastically reduce the sampling domain and ensure consistency of unit operations. Moreover, we present a methodology for constructing surrogate models based on penalized regression and error-maximization sampling. All these algorithms have been implemented as a free and open-source software package.Through a case study on the water-gas shift reaction for hydrogen production, we show that sampling domain reduction reduces the required number of sampling points significantly and improves the accuracy of the surrogate model.

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Section: Surrogate Model Developmentmentioning

confidence: 98%

Section: Surrogate Model Developmentmentioning

confidence: 99%

mentioning

confidence: 99%

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Constrained adaptive sampling for domain reduction in surrogate model generation: Applications to hydrogen production

et al. 2021

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“…In addition, an efficient optimization algorithm for identifying the new sample and the leave-one-out cross validation weightings are also discussed. Garbo and German used nonlinearity index as the refinement metric and the mean neighborhood distance for exploration. They balanced the two metrics via a stochastic Pareto-ranking-based selection criterion that attempts to simultaneously maximize both refinement and exploration.…”

Section: Literature Review and Analysismentioning

confidence: 99%

Method of Hybrid Adaptive Sampling for the Kriging Metamodel and Application in the Hydropurification Process of Industrial Terephthalic Acid

Cheng

et al. 2020

Ind. Eng. Chem. Res.

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The high-fidelity model is not easy to analyze and optimize because of the high computational cost. When the classical design of experiments is applied to construct the metamodel to replace such a computationally intensive model for analysis or optimization, it usually needs more samples compared with hybrid adaptive sampling to ensure the reliability of the metamodel due to ignoring the system information. In this study, considering the general feature of the chemical model, a new method of hybrid adaptive sampling named hybrid adaptive sampling algorithm based on scores (HASAS) is proposed on the basis of k-nearest neighbor for exploration and nearest neighbor expected improvement for exploitation to enhance the global quality of the metamodel. Furthermore, a weight coefficient is introduced to balance exploration and exploitation during sample placement. Sixteen benchmark cases are utilized to evaluate the performance of the HASAS and four other hybrid adaptive methods. Results show that with the same number of samples, HASAS can perform well in terms of global accuracy on most of them. The effect of the number of initial sample points on HASAS is also discussed. Finally, it is applied for the construction of the Kriging for the hydropurification process of a typical chemical (terephthalic acid) and sensitivity of each variable is done with the metamodel.

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“…To ensure that models meet the accuracy requirements, it is common to iteratively refine them through adaptive sampling sometimes also labeled as infill or active learning. Various criteria are available, and the interested reader is referred to the literature for a more in-depth introduction [12][13][14]. Accounting for all levels of fidelity at hand during the adaptive sampling stage should result in reduced overall computational cost necessary to perform a certain investigation, as the available information is used efficiently.…”

Section: Introductionmentioning

confidence: 99%

Multi-Fidelity Adaptive Sampling for Surrogate-Based Optimization and Uncertainty Quantification

Garbo,

Parekh,

Rischmann

et al. 2024

Aerospace

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Surrogate-based algorithms are indispensable in the aerospace engineering field for reducing the computational cost of optimization and uncertainty quantification analyses, particularly those involving computationally intensive solvers. This paper presents a novel approach for enhancing the efficiency of surrogate-based algorithms through a new multi-fidelity sampling technique. Unlike existing multi-fidelity methods which are based on a single multiplicative acquisition function, the proposed technique decouples the identification of the new infill sample from the selection of the fidelity level. The location of the infill sample is determined by leveraging the highest fidelity surrogate model, while the fidelity level used for its performance evaluation is chosen as the cheapest one within the “accurate enough” models at the infill location. Moreover, the methodology introduces the application of the Jensen–Shannon divergence to quantify the accuracy of the different fidelity levels. Overall, the resulting technique eliminates some of the drawbacks of existing multiplicative acquisition functions such as the risk of continuous sampling from lower and cheaper fidelity levels. Experimental validation conducted in surrogate-based optimization and uncertainty quantification scenarios demonstrates the efficacy of the proposed approach. In an aerodynamic shape optimization task focused on maximizing the lift-to-drag ratio, the multi-fidelity strategy achieved comparable results to standard single-fidelity sampling but with approximately a five-fold improvement in computational efficiency. Likewise, a similar reduction in computational costs was observed in the uncertainty quantification problem, with the resulting statistical values aligning closely with those obtained using traditional single-fidelity sampling.

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A model-independent adaptive sequential sampling technique based on response nonlinearity estimation

Cited by 8 publications

References 41 publications

Constrained adaptive sampling for domain reduction in surrogate model generation: Applications to hydrogen production

Constrained adaptive sampling for domain reduction in surrogate model generation: Applications to hydrogen production

Method of Hybrid Adaptive Sampling for the Kriging Metamodel and Application in the Hydropurification Process of Industrial Terephthalic Acid

Multi-Fidelity Adaptive Sampling for Surrogate-Based Optimization and Uncertainty Quantification

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