12 citations · 22 across the 12 of their papers we have counts for
15 papers
Optimal Monte Carlo integration on matrix manifolds
Aleksei G. Sorokin, Yihan Guo, Lek-Heng Lim
This article develops optimal order (up to logarithmic factors) weighted cubature rules for Monte Carlo numerical integration in Sobolev reproducing kernel Hilbert spaces over comm…
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…
Algorithms and Scientific Software for Quasi-Monte Carlo, Fast Gaussian Process Regression, and Scientific Machine Learning
Aleksei G. Sorokin
Most scientific domains elicit the development of efficient algorithms and accessible scientific software. This thesis unifies our developments in three broad domains: Quasi-Monte…
Operator Learning at Machine Precision
Aras Bacho, Aleksei G. Sorokin, Xianjin Yang +6
Neural operator learning methods have garnered significant attention in scientific computing for their ability to approximate infinite-dimensional operators. However, increasing th…
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…
Massive Star Formation at Supersolar Metallicities: Constraints on the Initial Mass Function
Eda Gjergo, Zhiyu Zhang, Pavel Kroupa +6
Metals enhance the cooling efficiency of molecular clouds, promoting fragmentation. Consequently, increasing the metallicity may boost the formation of low-mass stars. Within the i…
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:…
On Bounding and Approximating Functions of Multiple Expectations using Quasi-Monte Carlo
Aleksei G. Sorokin, Jagadeeswaran Rathinavel
Monte Carlo and Quasi-Monte Carlo methods present a convenient approach for approximating the expected value of a random variable. Algorithms exist to adaptively sample the random…
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.…
Credible Intervals for Probability of Failure with Gaussian Processes
Aleksei G. Sorokin, Vishwas Rao
Estimating the probability of failure for expensive simulations is a central task in reliability analysis for structural design, power grid design, and safety certification, among…
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…
SigOpt Mulch: An Intelligent System for AutoML of Gradient Boosted Trees
Aleksei Sorokin, Xinran Zhu, Eric Hans Lee +1
Gradient boosted trees (GBTs) are ubiquitous models used by researchers, machine learning (ML) practitioners, and data scientists because of their robust performance, interpretable…
GalCEM I -- An Open-Source Detailed Isotopic Chemical Evolution Code
Eda Gjergo, Aleksei G. Sorokin, Anthony Ruth +8
This is the first of a series of papers that will introduce a user-friendly, detailed, and modular GALactic Chemical Evolution Model, GalCEM, that tracks isotope masses as a functi…
Quasi-Monte Carlo Software
Sou-Cheng T. Choi, Fred J. Hickernell, R. Jagadeeswaran +2
Practitioners wishing to experience the efficiency gains from using low discrepancy sequences need correct, robust, well-written software. This article, based on our MCQMC 2020 tut…