Results 61 to 70 of about 2,168,026 (221)

nl-Selective Classical Charge-Exchange Cross Sections in Be4+ and Ground State Hydrogen Atom Collisions

open access: yesAtoms, 2022
Charge-exchange cross sections in Be4+ + H(1s) collisions are calculated using the three-body classical trajectory Monte Carlo method (CTMC) and the quasi-classical trajectory Monte Carlo method of Kirschbaum and Wilets (QCTMC) for impact energies ...
Iman Ziaeian, Károly Tőkési
doaj   +1 more source

Experimental study of 𝛽 spectra using Si detectors [PDF]

open access: yesEPJ Web of Conferences, 2020
Several scientific users from different communities, such as nuclear medicine, ionizing radiation metrology, nuclear energy industries, and fundamental physics are seeking for a precise knowledge of beta spectra.
Singh Abhilasha   +4 more
doaj   +1 more source

Adaptive Search in Quasi-Monte-Carlo Optimization [PDF]

open access: yesMathematics of Computation, 1995
Motivated by a linear time-complexity result for an adaptive Monte Carlo algorithm, we propose and analyze an adaptive deterministic algorithm. We restrict a grid search to nested subregions that promise to provide improvement of the current solution, and we obtain an exponential rate of convergence in function evaluations.
Biester, Christian   +3 more
openaire   +2 more sources

Construction of Good Rank-1 Lattice Rules Based on the Weighted Star Discrepancy [PDF]

open access: yes, 2006
The ‘goodness’ of a set of quadrature points in [0, 1]d may be measured by the weighted star discrepancy. If the weights for the weighted star discrepancy are summable, then we show that for n prime there exist n-point rank-1 lattice rules whose weighted
Joe, Stephen
core   +1 more source

Why Monte Carlo Simulations are Inferences and not Experiments [PDF]

open access: yes, 2012
Monte Carlo Simulations arrive at their results by introducing randomness, sometimes derived from a physical randomizing device. Nonetheless, we argue, they open no new epistemic channels beyond that already employed by traditional simulations: the ...
John D. Norton   +3 more
core   +2 more sources

Estimation of the Craniectomy Surface Area by Using Postoperative Images

open access: yesInternational Journal of Biomedical Imaging, 2018
Decompressive craniectomy (DC) is a neurosurgical procedure performed to relieve the intracranial pressure engendered by brain swelling. However, no easy and accurate method exists for determining the craniectomy surface area.
Meng-Yin Ho, Wei-Lung Tseng, Furen Xiao
doaj   +1 more source

Pin-by-Pin Coupled Transient Monte Carlo Analysis Using the iMC Code

open access: yesFrontiers in Energy Research, 2022
In this article, we present a coupled multi-physics Monte Carlo reactor transient analysis framework implemented in the KAIST Monte Carlo iMC code. In the multi-physics framework, the time-dependent neutron transport calculation and the transient heat ...
HyeonTae Kim, Yonghee Kim
doaj   +1 more source

Quasi-Monte Carlo hyperinterpolation

open access: yesJournal of Computational and Applied Mathematics
This paper studies a generalization of hyperinterpolation over the high-dimensional unit cube. Hyperinterpolation of degree \( m \) serves as a discrete approximation of the \( L_2 \)-orthogonal projection of the same degree, using Fourier coefficients evaluated by a positive-weight quadrature rule that exactly integrates all polynomials of degree up ...
Congpei An, Mou Cai, Takashi Goda
openaire   +3 more sources

Sufficient Conditions for Central Limit Theorems and Confidence Intervals for Randomized Quasi-Monte Carlo Methods

open access: yesACM Transactions on Modeling and Computer Simulation
Randomized quasi-Monte Carlo methods have been introduced with the main purpose of yielding a computable measure of error for quasi-Monte Carlo approximations through the implicit application of a central limit theorem over independent randomizations ...
Marvin K. Nakayama, Bruno Tuffin
semanticscholar   +1 more source

Quasi-Monte Carlo Variational Inference

open access: yesCoRR, 2018
Many machine learning problems involve Monte Carlo gradient estimators. As a prominent example, we focus on Monte Carlo variational inference (MCVI) in this paper. The performance of MCVI crucially depends on the variance of its stochastic gradients. We propose variance reduction by means of Quasi-Monte Carlo (QMC) sampling.
Alexander Buchholz   +2 more
openaire   +4 more sources

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