References in zbMATH (referenced in 82 articles , 1 standard article )

Showing results 1 to 20 of 82.
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  1. Chen, Jialei; Chen, Zhehui; Zhang, Chuck; Jeff Wu, C. F.: APIK: active physics-informed Kriging model with partial differential equations (2022)
  2. Mohammadi, Hossein; Challenor, Peter; Williamson, Daniel; Goodfellow, Marc: Cross-validation-based adaptive sampling for Gaussian process models (2022)
  3. Allaire, Frédéric; Mallet, Vivien; Filippi, Jean-Baptiste: Novel method for a posteriori uncertainty quantification in wildland fire spread simulation (2021)
  4. Balata, Alessandro; Ludkovski, Michael; Maheshwari, Aditya; Palczewski, Jan: Statistical learning for probability-constrained stochastic optimal control (2021)
  5. Chevalier, Clément; Martius, Olivia; Ginsbourger, David: Modeling nonstationary extreme dependence with stationary max-stable processes and multidimensional scaling (2021)
  6. Evandro Konzen, Yafeng Cheng, Jian Qing Shi: Gaussian Process for Functional Data Analysis: The GPFDA Package for R (2021) arXiv
  7. Mastrippolito, Franck; Aubert, Stéphane; Ducros, Frédéric: Kriging metamodels-based multi-objective shape optimization applied to a multi-scale heat exchanger (2021)
  8. Mickael Binois, Robert B. Gramacy: hetGP: Heteroskedastic Gaussian Process Modeling and Sequential Design in R (2021) not zbMATH
  9. Nikiforova, Nedka Dechkova; Berni, Rossella; Arcidiacono, Gabriele; Cantone, Luciano; Placidoli, Pierpaolo: Latin hypercube designs based on strong orthogonal arrays and Kriging modelling to improve the payload distribution of trains (2021)
  10. Xiao, Qian; Xu, Hongquan: A mapping-based universal kriging model for order-of-addition experiments in drug combination studies (2021)
  11. Yang, Yang; Ji, Chunlin; Deng, Ke: Rapid design of metamaterials via multitarget Bayesian optimization (2021)
  12. Azaïs, Jean-Marc; Bachoc, François; Lagnoux, Agnès; Nguyen, Thi Mong Ngoc: Semi-parametric estimation of the variogram scale parameter of a Gaussian process with stationary increments (2020)
  13. Bachoc, François; Helbert, Céline; Picheny, Victor: Gaussian process optimization with failures: classification and convergence proof (2020)
  14. Binois, Mickaël; Ginsbourger, David; Roustant, Olivier: On the choice of the low-dimensional domain for global optimization via random embeddings (2020)
  15. El Amri, Mohamed Reda; Helbert, Céline; Lepreux, Olivier; Zuniga, Miguel Munoz; Prieur, Clémentine; Sinoquet, Delphine: Data-driven stochastic inversion via functional quantization (2020)
  16. Gahrooei, Mostafa Reisi; Yan, Hao; Paynabar, Kamran: Comments on: “On active learning methods for manifold data” (2020)
  17. Guan, Qian; Reich, Brian J.; Laber, Eric B.; Bandyopadhyay, Dipankar: Bayesian nonparametric policy search with application to periodontal recall intervals (2020)
  18. Hu, Ruimeng: Deep learning for ranking response surfaces with applications to optimal stopping problems (2020)
  19. López-Lopera, Andrés F.; Bachoc, François; Durrande, Nicolas; Rohmer, Jérémy; Idier, Déborah; Roustant, Olivier: Approximating Gaussian process emulators with linear inequality constraints and noisy observations via MC and MCMC (2020)
  20. Lu, Xuefei; Rudi, Alessandro; Borgonovo, Emanuele; Rosasco, Lorenzo: Faster Kriging: facing high-dimensional simulators (2020)

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