Jan 5, 2024 · The surrogate is simply the posterior function that the gp gives you. What you do with that posterior and the work flow you build around it is what leads to ...

Jul 20, 2023 · In this research, Gaussian Process Regression (GPR) was used as a surrogate to minimize the time costs of the agent-based model, as well as to demonstrate the ...

Feb 22, 2024 · In this context, the Gaussian Process regression (also called kriging) is one of the most popular technique. It offers the advantage of providing a predictive ...

Jan 5, 2024 · So far I have used Gaussian process models mainly for Bayesian optimization. As others have mentioned, Bayesian optimization methods are in general more ...

Jul 11, 2023 · The Gaussian process (GP) model, also known as the Kriging model, is a popular surrogate model that gives an approximate prediction of the QoIs, is ...

Duration: 1:10:17
Posted: Jun 3, 2024

Aug 12, 2023 · Abstract—Active Learning of Gaussian process (GP) sur- rogates is an efficient way to model unknown environments in various applications.

Apr 10, 2024 · To reduce the computational burden, multivariate Gaussian process regression (MVGPR) is proposed as a surrogate model that only requires computing a small ...

Oct 17, 2023 · As a surrogate, we construct a Gaussian process regression model. We measure the global approximation error in terms of its impact on the accuracy of the ...