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Title: Neural Network Algorithm for Particle Loading

Abstract

An artificial neural network algorithm for continuous minimization is developed and applied to the case of numerical particle loading. It is shown that higher-order moments of the probability distribution function can be efficiently renormalized using this technique. A general neural network for the renormalization of an arbitrary number of moments is given.

Authors:
Publication Date:
Research Org.:
Princeton Plasma Physics Lab., NJ (US)
Sponsoring Org.:
USDOE Office of Science (SC) (US)
OSTI Identifier:
813621
Report Number(s):
PPPL-3808
TRN: US0303954
DOE Contract Number:  
AC02-76CH03073
Resource Type:
Technical Report
Resource Relation:
Other Information: PBD: 25 Apr 2003
Country of Publication:
United States
Language:
English
Subject:
12 MANAGEMENT OF RADIOACTIVE WASTES, AND NON-RADIOACTIVE WASTES FROM NUCLEAR FACILITIES; 70 PLASMA PHYSICS AND FUSION TECHNOLOGY; ALGORITHMS; DISTRIBUTION FUNCTIONS; MINIMIZATION; NEURAL NETWORKS; PROBABILITY; RENORMALIZATION; MONTE CARLO METHOD; MATHEMATICAL PHYSICS; MONTE CARLO METHODS; NUMERICAL SIMULATION

Citation Formats

V Lewandowski, J L. Neural Network Algorithm for Particle Loading. United States: N. p., 2003. Web. doi:10.1016/S0375-9601(03)00769-2.
V Lewandowski, J L. Neural Network Algorithm for Particle Loading. United States. doi:10.1016/S0375-9601(03)00769-2.
V Lewandowski, J L. Fri . "Neural Network Algorithm for Particle Loading". United States. doi:10.1016/S0375-9601(03)00769-2. https://www.osti.gov/servlets/purl/813621.
@article{osti_813621,
title = {Neural Network Algorithm for Particle Loading},
author = {V Lewandowski, J L},
abstractNote = {An artificial neural network algorithm for continuous minimization is developed and applied to the case of numerical particle loading. It is shown that higher-order moments of the probability distribution function can be efficiently renormalized using this technique. A general neural network for the renormalization of an arbitrary number of moments is given.},
doi = {10.1016/S0375-9601(03)00769-2},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2003},
month = {4}
}