5 citations · 6 across the 7 of their papers we have counts for
18 papers
Fast multitask Gaussian processes, with application to surrogate modeling of the quark-gluon plasma
Aleksei G. Sorokin, Pieterjan Robbe, Yen-Chun Liu +2
Gaussian processes (GPs) are broadly used for the surrogate modeling of computer experiments with reliable uncertainty quantification. Our motivating application comes from the stu…
Fast Bayesian Multilevel Quasi-Monte Carlo
Aleksei G. Sorokin, Pieterjan Robbe, Gianluca Geraci +2
Existing multilevel quasi-Monte Carlo (MLQMC) methods often rely on multiple independent randomizations of a low-discrepancy (LD) sequence to estimate statistical errors on each le…
Empirical Bernstein and betting confidence intervals for randomized quasi-Monte Carlo
Aadit Jain, Fred J. Hickernell, Art B. Owen +1
Randomized quasi-Monte Carlo (RQMC) methods estimate the mean of a random variable by sampling an integrand at equidistributed points. For scrambled digital nets, the resulting…
Quasi-Monte Carlo Methods: What, Why, and How?
Fred J. Hickernell, Nathan Kirk, Aleksei G. Sorokin
Many questions in quantitative finance, uncertainty quantification, and other disciplines are answered by computing the population mean, , where instances of $Y:…
Challenges in Developing Great Quasi-Monte Carlo Software
Sou-Cheng T. Choi, Yuhan Ding, Fred J. Hickernell +2
Quasi-Monte Carlo (QMC) methods have developed over several decades. With the explosion in computational science, there is a need for great software that implements QMC algorithms.…
Computationally Efficient and Error Aware Surrogate Construction for Numerical Solutions of Subsurface Flow Through Porous Media
Aleksei G. Sorokin, Aleksandra Pachalieva, Daniel O'Malley +3
Limiting the injection rate to restrict the pressure below a threshold at a critical location can be an important goal of simulations that model the subsurface pressure between inj…