Results 281 to 290 of about 17,312,778 (333)

Neural Control Variates with Automatic Integration

International Conference on Computer Graphics and Interactive Techniques
This paper presents a method to leverage arbitrary neural network architecture for control variates. Control variates are crucial in reducing the variance of Monte Carlo integration, but they hinge on finding a function that both correlates with the ...
Zilu Li, Guandao Yang, Qingqing Zhao, Xi Deng, Leonidas J. Guibas, Bharath Hariharan, Gordon Wetzstein   +6 more
semanticscholar   +3 more sources

Scalable Control Variates for Monte Carlo Methods via Stochastic Optimization [PDF]

Monte Carlo and Quasi-Monte Carlo Methods, 2020
Control variates are a well-established tool to reduce the variance of Monte Carlo estimators. However, for large-scale problems including high-dimensional and large-sample settings, their advantages can be outweighed by a substantial computational cost.
Shijing Si, C. Oates, A. Duncan, L. Carin, F. Briol   +4 more
semanticscholar   +2 more sources

Some of the next articles are maybe not open access.

Related searches:

First order control variates algorithm for reliability analysis of engineering structures

Applied Mathematical Modelling, 2020
This study introduces an efficient method for safety evaluation of the structures with small failure probability. The proposed approach reformulates basic failure probability formula based on control variates method and suggests to firstly substitute the
Mansour Ghalehnovi, Mohsen Rashki
exaly   +2 more sources

Combining antithetic variates and control variates in simulation experiments

ACM Transactions on Modeling and Computer Simulation, 1996
Antithetic variates and control variates are two well-known variance reduction techniques. We consider combining antithetic variates and control variates to estimate the mean response in a stochastic simulation experiment. When applying antithetic variates to generate control variates across paired replications, we show that the integrated control ...
Philip Heidelberger
exaly   +4 more sources

Modified Knowledge-Based Neural Networks Using Control Variates for the Fast Uncertainty Quantification of On-Chip MWCNT Interconnects

IEEE transactions on electromagnetic compatibility (Print), 2023
In this article, a modified knowledge-based artificial neural network (KBANN) metamodel is developed for the efficient uncertainty quantification of on-chip multiwalled carbon nanotube (MWCNT) interconnects.
K. Dimple, Surila Guglani, A. Dasgupta, Rohit Sharma, Sourajeet Roy, Brajesh Kumar Kaushik   +5 more
semanticscholar   +1 more source

Concepts of variation control systems

Journal of Systems and Software, 2021
Abstract Version control systems are an integral part of today’s software engineering. They facilitate the collaborative management of revisions (sequential versions) and variants (concurrent versions) of software systems under development. Typical version control systems maintain revisions of files and variants of whole software systems.
Lukas Linsbauer, Felix Schwägerl, Thorsten Berger, Paul Grünbacher   +3 more
openaire   +1 more source

Adaptive fully sequential selection procedures with linear and nonlinear control variates

IISE Transactions, 2022
A decision-making process often involves selecting the best solution from a finite set of possible alternatives regarding some performance measure, which is known as Ranking-and-Selection (R&S) when the performance is not explicitly available and can ...
S. Tsai, Jun Luo, Guangxin Jiang, W. Yeh
semanticscholar   +1 more source

Mobility estimation for Langevin dynamics using control variates

Multiscale Modeling & simulation, 2022
The scaling of the mobility of two-dimensional Langevin dynamics in a periodic potential as the friction vanishes is not well understood for non-separable potentials.
G. Pavliotis, G. Stoltz, U. Vaes
semanticscholar   +1 more source

Efficient estimation of multiple expectations with the same sample by adaptive importance sampling and control variates

Statistics and computing, 2022
Some classical uncertainty quantification problems require the estimation of multiple expectations. Estimating all of them accurately is crucial and can have a major impact on the analysis to perform, and standard existing Monte Carlo methods can be ...
J. Demange-Chryst, François Bachoc, J. Morio   +2 more
semanticscholar   +1 more source

Reducing uncertainty in time domain fatigue analysis of offshore structures using control variates

, 2021
This study is concerned with time domain fatigue analysis of offshore structures subjected to random waves. The fatigue damage calculated from a single realization of the stress time history is random, thus the mean damage is typically estimated via ...
Ruifeng Chen, Y. Low
semanticscholar   +1 more source