Abstract
The Monte Carlo neutronics analysis requires the capability for a tally distribution estimation like an axial power distribution or a flux gradient in a fuel rod, etc. This problem can be regarded as a probability density function estimation from an observation set. We apply the neural network based density estimation method to an observation and sampling weight set produced by the Monte Carlo calculations. The neural network method is compared with the histogram and the functional expansion tally method for estimating a non-smooth density, a fission source distribution, and an absorption rate's gradient in a burnable absorber rod. The application results shows that the neural network method can approximate a tally distribution quite well. (authors)
Shim, Hyung Jin;
Cho, Jin Young;
Song, Jae Seung;
[1]
Kim, Chang Hyo
[2]
- Korea Atomic Energy Research Institute, 1045 Daedeokdaero, Yuseong-gu, Daejeon 305-353 (Korea, Republic of)
- Seoul National University, San 56-1, Shillim-dong, Gwanak-gu, Seoul 151-742 (Korea, Republic of)
Citation Formats
Shim, Hyung Jin, Cho, Jin Young, Song, Jae Seung, and Kim, Chang Hyo.
Probability Density Estimation Using Neural Networks in Monte Carlo Calculations.
Switzerland: N. p.,
2008.
Web.
Shim, Hyung Jin, Cho, Jin Young, Song, Jae Seung, & Kim, Chang Hyo.
Probability Density Estimation Using Neural Networks in Monte Carlo Calculations.
Switzerland.
Shim, Hyung Jin, Cho, Jin Young, Song, Jae Seung, and Kim, Chang Hyo.
2008.
"Probability Density Estimation Using Neural Networks in Monte Carlo Calculations."
Switzerland.
@misc{etde_21391777,
title = {Probability Density Estimation Using Neural Networks in Monte Carlo Calculations}
author = {Shim, Hyung Jin, Cho, Jin Young, Song, Jae Seung, and Kim, Chang Hyo}
abstractNote = {The Monte Carlo neutronics analysis requires the capability for a tally distribution estimation like an axial power distribution or a flux gradient in a fuel rod, etc. This problem can be regarded as a probability density function estimation from an observation set. We apply the neural network based density estimation method to an observation and sampling weight set produced by the Monte Carlo calculations. The neural network method is compared with the histogram and the functional expansion tally method for estimating a non-smooth density, a fission source distribution, and an absorption rate's gradient in a burnable absorber rod. The application results shows that the neural network method can approximate a tally distribution quite well. (authors)}
place = {Switzerland}
year = {2008}
month = {Jul}
}
title = {Probability Density Estimation Using Neural Networks in Monte Carlo Calculations}
author = {Shim, Hyung Jin, Cho, Jin Young, Song, Jae Seung, and Kim, Chang Hyo}
abstractNote = {The Monte Carlo neutronics analysis requires the capability for a tally distribution estimation like an axial power distribution or a flux gradient in a fuel rod, etc. This problem can be regarded as a probability density function estimation from an observation set. We apply the neural network based density estimation method to an observation and sampling weight set produced by the Monte Carlo calculations. The neural network method is compared with the histogram and the functional expansion tally method for estimating a non-smooth density, a fission source distribution, and an absorption rate's gradient in a burnable absorber rod. The application results shows that the neural network method can approximate a tally distribution quite well. (authors)}
place = {Switzerland}
year = {2008}
month = {Jul}
}