Multi-fidelity Gaussian process regression for computer experiments
By Loic Le Gratiet. 2013
114 citations recorded by the source corpus.
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Citations in the corpus
- Neural-net-induced Gaussian process regression for function approximation and PDE solution (2018, background): “After prescribing the covariance function k o ( · , · ) for computing the covariance matrix K oo and then training the regression model by the MLE, we derive the function approximation by using formula (13).”
- Machine learning of linear differential equations using Gaussian processes (2017, methodology): “The methodology can be straightforwardly generalized to address data with multiple levels of fidelity [24, 39] and equations with variable coefficients and complex geometries.”
- A Python surrogate modeling framework with derivatives (2019, methodology): “The high-fidelity model is given by = +y x x y x x( ) ( ) ( ) ( ).high low SMT follows the formulation proposed by Le Gratiet [22], which is recursive and can be easily extended to multiple levels of fidelity.”
- Multi-fidelity Gaussian process regression for prediction of random fields (2017, methodology): “where ã j−1)(ξ) and z j)(ξ) are appropriate Gaussian random fields, while ρ j−1)(ξ) are deterministic scaling fields (see [15,14,13] for further details).”
- Review of multi-fidelity models (2016, methodology): “This data fusion leads to more efficient low-and high-fidelity data utilization in constructing a unified predictive model [130, 128].”
- Discovering variable fractional orders of advection-dispersion equations from field data using multi-fidelity Bayesian optimization (2017, background): “, Gaussian, and the prior distribution for signal variances and trend coefficients is taken as the non-informative Jeffreys distribution having no extra parameters to be tuned [17].”