Results 31 to 40 of about 1,419,392 (377)
INLINE THERMAL AND XENON FEEDBACK ITERATIONS IN MONTE CARLO REACTOR CALCULATIONS [PDF]
EPJ Web of Conferences, 2021 In this work, we describe a method for converging nonlinear feedback during the convergence of the neutron fission source in a Monte Carlo reactor simulation.
Gill Daniel F.
doaj +1 more source
PENELOPE 2018: A code system for Monte Carlo simulation of electron and photon transport
PENELOPE: A code system for Monte Carlo simulation of electron and photon transport, 2019 The computer code system penelope (version 2018) performs Monte Carlo simulation of coupled electron-photon transport in arbitrary materials for a wide energy range, from a few hundred eV to about 1 GeV.
semanticscholar +1 more source
The Coupled Electronic-Ionic Monte Carlo Simulation Method [PDF]
, 2002 Quantum Monte Carlo (QMC) methods such as Variational Monte Carlo, Diffusion Monte Carlo or Path Integral Monte Carlo are the most accurate and general methods for computing total electronic energies.
B. L. Hammond Jr. +35 more
core +2 more sources
Introduction to Monte Carlo Simulation [PDF]
AIP Conference Proceedings, 2010 This paper reviews the history and principles of Monte Carlo simulation, emphasizing techniques commonly used in the simulation of medical imaging.
Robert L. Harrison +2 more
openaire +3 more sources
Monte-Carlo-Based Estimation of the X-ray Energy Spectrum for CT Artifact Reduction
Applied Sciences, 2021 Beam hardening and scattering effects can seriously degrade image quality in polychromatic X-ray CT imaging. In recent years, polychromatic image reconstruction techniques and scatter estimation using Monte Carlo simulation have been developed to ...
Ehsan Nazemi +5 more
doaj +1 more source
Data‐driven performance metrics for neural network learning
International Journal of Adaptive Control and Signal Processing, EarlyView., 2023 Summary Effectiveness of data‐driven neural learning in terms of both local mimima trapping and convergence rate is addressed. Such issues are investigated in a case study involving the training of one‐hidden‐layer feedforward neural networks with the extended Kalman filter, which reduces the search for the optimal network parameters to a state ...
Angelo Alessandri +2 more
wiley +1 more source
Reducing quasi-ergodicity in a double well potential by Tsallis Monte Carlo simulation [PDF]
Physica A278, 414-427 (2000), 2000 A new Monte Carlo scheme based on the system of Tsallis's generalized statistical mechanics is applied to a simple double well potential to calculate the canonical thermal average of potential energy. Although we observed serious quasi-ergodicity when using the standard Metropolis Monte Carlo algorithm, this problem is largely reduced by the use of the
arxiv +1 more source
Monte Carlo Simulation and Applications
Journal of Kufa for Mathematics and Computer, 2012 The Monte Carlo simulation technique is one of the common computational tools used to imitate and follow up complex real life systems and their development with time. Variables of a disease problem were defined and the mathematical model for this problem
Abd Al Kareem I Sheet, Nadia Adeel Saeed
doaj +1 more source
Improved neural network Monte Carlo simulation
SciPost Physics, 2021 The algorithm for Monte Carlo simulation of parton-level events based on an Artificial Neural Network (ANN) proposed in arXiv:1810.11509 is used to perform a simulation of $H\to 4\ell$ decay.
I-Kai Chen, Matthew D. Klimek, Maxim Perelstein
doaj +1 more source
Conversion of Monte Carlo Steps to Real Time for Grain Growth Simulation
Advances in Mathematical Physics, 2017 Monte Carlo (MC) technique is becoming a very effective simulation method for prediction and analysis of the grain growth kinetics at mesoscopic level.
N. Maazi
doaj +1 more source