Virtual library

Virtual library of simulation experiments: Test functions and datasets. This website is intended to be used as a suite of functions and datasets for evaluating new approaches to the design and analysis of experiments involving computer models. Under each of the categories found on the left, there is a list of corresponding functions and datasets. Each has a detailed description, as well as implementations in both MATLAB and R: Optimization, Emulation/ Prediction, Uncertainty Quantification, Multi Fidelity Simulation, Calibration/ Tuning, Screening, Integration, Functional Data.


References in zbMATH (referenced in 23 articles )

Showing results 1 to 20 of 23.
Sorted by year (citations)

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  1. Davis, Casey B.; Hans, Christopher M.; Santner, Thomas J.: Prediction of non-stationary response functions using a Bayesian composite Gaussian process (2021)
  2. G.-Tóth, B.; Casado, L. G.; Hendrix, E. M. T.; Messine, F.: On new methods to construct lower bounds in simplicial branch and bound based on interval arithmetic (2021)
  3. Hokanson, Jeffrey M.; Constantine, Paul G.: A Lipschitz matrix for parameter reduction in computational science (2021)
  4. Luo, Xin-long; Xiao, Hang: Generalized continuation Newton methods and the trust-region updating strategy for the underdetermined system (2021)
  5. Luo, Xin-long; Xiao, Hang; Lv, Jia-hui; Zhang, Sen: Explicit pseudo-transient continuation and the trust-region updating strategy for unconstrained optimization (2021)
  6. Yang, Zebin; Zhang, Aijun: Hyperparameter optimization via sequential uniform designs (2021)
  7. Giladi, Chen; Sintov, Avishai: Manifold learning for efficient gravitational search algorithm (2020)
  8. Valadão, Mônica A. C.; Batista, Lucas S.: A comparative study on surrogate models for SAEAs (2020)
  9. Glaws, Andrew; Constantine, Paul G.: Gauss-Christoffel quadrature for inverse regression: applications to computer experiments (2019)
  10. Gu, Mengyang: Jointly robust prior for Gaussian stochastic process in emulation, calibration and variable selection (2019)
  11. Owen, Art B.: Comment: unreasonable effectiveness of Monte Carlo (2019)
  12. Sun, Furong; Gramacy, Robert B.; Haaland, Benjamin; Lawrence, Earl; Walker, Andrew: Emulating satellite drag from large simulation experiments (2019)
  13. Brauchart, Johann S.: Explicit families of functions on the sphere with exactly known Sobolev space smoothness (2018)
  14. Erickson, Collin B.; Ankenman, Bruce E.; Sanchez, Susan M.: Comparison of Gaussian process modeling software (2018)
  15. Gorodetsky, Alex A.; Jakeman, John D.: Gradient-based optimization for regression in the functional tensor-train format (2018)
  16. Gu, Mengyang; Wang, Xiaojing; Berger, James O.: Robust Gaussian stochastic process emulation (2018)
  17. Perdikaris, P.; Raissi, M.; Damianou, A.; Lawrence, N. D.; Karniadakis, G. E.: Nonlinear information fusion algorithms for data-efficient multi-fidelity modelling (2017)
  18. Wedyan, Ahmad; Whalley, Jacqueline; Narayanan, Ajit: Hydrological cycle algorithm for continuous optimization problems (2017)
  19. Guzman, Yannis A.; Faruque Hasan, M. M.; Floudas, Christodoulos A.: Performance of convex underestimators in a branch-and-bound framework (2016)
  20. Perdikaris, Paris; Venturi, Daniele; Karniadakis, George Em: Multifidelity information fusion algorithms for high-dimensional systems and massive data sets (2016)

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