Gaussian process (GP) surrogate modeling for large computer experiments is limited by
cubic runtimes, especially with data from stochastic simulations with input-dependent noise …

We introduce mlOSP, a computational template for Machine Learning for Optimal Stopping
Problems. The template is implemented in the R statistical environment and publicly …

This paper proposes two sequential metamodel‐based methods for level‐set estimation
(LSE) that leverage the uniform bound built on stochastic kriging: predictive variance …

This article focuses on the multi-objective optimization of stochastic simulators with high
output variance, where the input space is finite and the objective functions are expensive to …

We introduce mlOSP, a computational template for machine learning for optimal stopping
problems, which is implemented in the R statistical environment and publicly available via a …

Heteroscedastic metamodeling has gained popularity as an effective tool for analyzing and
optimizing complex stochastic systems. A heteroscedastic metamodel provides an accurate …

This paper designs a new batching program to extract the original data, which helps to
traverse the entire sample space quickly and provides a new approach for data extraction …