Knowl-Based Syst 134:199–212.Consecutive sampling is defined as a non-probability sampling technique where samples are picked at the ease of a researcher more like convenience sampling, only with a slight variation. Zhou Q, Wang Y, Choi S-K, Jiang P, Shao X, Hu J (2017) A sequential multi-fidelity metamodeling approach for data regression. Xiong S, Qian PZG, Wu CFJ (2013) Sequential design and analysis of high-accuracy and low-accuracy computer codes. Xia H, Ding Y, Mallick BK (2011) Bayesian hierarchical model for combining misaligned two-resolution metrology data. Sun F, Wang Y, Xu H (2019) Uniform projection designs. Sobester KF (2008) Engineering design via surrogate modelling: a practical guide. Shewry MC, Wynn HP (1987) Maximum entropy sampling. Sheng C, Tan MHY, Zou L (2020) Maximum expected entropy transformed Latin hypercube designs. Shang B, Apley DW (2020) Fully-sequential space-filling design algorithms for computer experiments. Santner TJ, Williams BJ, Notz WI (2003) The design and analysis of computer experiments. Sacks J, Schiller SB, Welch WJ (1989) Designs for computer experiments. Qian Z, Seepersad CC, Joseph VR, Allen JK, Wu CFJ (2006) Building surrogate models based on detailed and approximate simulations. Qian PZG, Wu CFJ (2008) Bayesian hierarchical modeling for integrating low-accuracy and high-accuracy experiments. Qian PZG (2009) Nested Latin hypercube designs. Poloczek M, Wang J, Frazier P (2017) Multi-information source optimization. Park C, Haftka RT, Kim NH (2016) Remarks on multi-fidelity surrogates. Park J, Reveliotis SA, Bodner DA, Zhou C, Wu J, McGinnis LF (2001) High-fidelity rapid prototyping of 300 mm fabs through discrete event system modeling. Ouyang L, Han M, Ma Y, Wang M, Park C (2022) Simulation optimization using stochastic kriging with robust statistics. Mu W, Xiong S (2018) A class of space-filling designs and their projection properties. Li X, Wang X, Xiong S (2021) A sequential design strategy for integrating low-accuracy and high-accuracy computer experiments. Li Z, Tan MHY (2021) A gaussian process emulator based approach for Bayesian calibration of a functional input. Kennedy M, O’Hagan (2000) Predicting the output from a complex computer code when fast approximations are available. Joseph VR, Gul E, Ba S (2015) Maximum projection designs for computer experiments. Hebbal A, Brevault L, Balesdent M, Talbi E-G, Melab N (2021) Multi-fidelity modeling with different input domain definitions using deep gaussian processes. Gratiet LL, Cannamela C (2015) Cokriging-based sequential design strategies using fast cross-validation techniques for multi-fidelity computer codes. Goodall P, Sharpe R, West A (2019) A data-driven simulation to support remanufacturing operations. Gahrooei MR, Paynabar K, Pacella M, Colosimo BM (2019) An adaptive fused sampling approach of high-accuracy data in the presence of low-accuracy data. įernández-Godino MG, Park C, Kim NH, Haftka RT (2019) Issues in deciding whether to use multifidelity surrogates. The results demonstrate that the proposed approach outperforms the other three methods in terms of both the prediction accuracy of the final surrogate model and the uniformity in all subspaces of the two codes.Ĭhen H, Zhang Y, Yang X (2020) Uniform projection nested Latin hypercube designs. The performance of the proposed approach is illustrated through several numerical examples. We use the entropy theory to score the execution of fidelity for each version, such that the one who has a greater potential to improve the model accuracy will be selected. The second issue is directly connected with deciding which code to run in the next iteration. On the other hand, those samples that only appear in the LF simulation are obtained by the original maximum projection design. Note that the obtained HF data is also executed in LF codes to form a nested structure. For the first issue, we propose a weighted maximum projection criterion combining the uniformity metrics of the HF and LF experiments to select HF points, where the weights are totally data-driven. This article develops a sequential nested design for MF experiments that pays attention to both the space-filling properties in all subsets of factors and the best combination between the two levels of accuracy. Unfortunately, no systematic study has hitherto been done to deal with these two key issues simultaneously. In order to adequately utilize the popular sequential designs to improve the effectiveness of the MF method, two challenges involving good projection properties in the presence of effect sparsity and the sample allocation between the high-fidelity(HF) and low-fidelity (LF) codes remain to be addressed. A growing area of focus is using multi-fidelity(MF) simulations to predict the behavior of complex physical systems.
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