# 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}

}

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