Use of artificial neural networks for analysis of complex physical systems

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Mathematical models of physical systems are used, among other purposes, to improve our understanding of the behavior of physical systems, predict physical system response, and control the responses of systems. Phenomenological models are frequently used to simulate system behavior, but an alternative is available - the artificial neural network (ANN). The ANN is an inductive, or data-based model for the simulation of input/output mappings. The ANN can be used in numerous frameworks to simulate physical system behavior. ANNs require training data to learn patterns of input/output behavior, and once trained, they can be used to simulate system behavior within the ... continued below

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21 p.

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Benjamin, A.; Altman, B.; O`Gorman, C.; Rodeman, R. & Paez, T.L. December 31, 1996.

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This article is part of the collection entitled: Office of Scientific & Technical Information Technical Reports and was provided by UNT Libraries Government Documents Department to Digital Library, a digital repository hosted by the UNT Libraries. It has been viewed 32 times . More information about this article can be viewed below.

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  • Sandia National Laboratories
    Publisher Info: Sandia National Labs., Albuquerque, NM (United States)
    Place of Publication: Albuquerque, New Mexico

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Description

Mathematical models of physical systems are used, among other purposes, to improve our understanding of the behavior of physical systems, predict physical system response, and control the responses of systems. Phenomenological models are frequently used to simulate system behavior, but an alternative is available - the artificial neural network (ANN). The ANN is an inductive, or data-based model for the simulation of input/output mappings. The ANN can be used in numerous frameworks to simulate physical system behavior. ANNs require training data to learn patterns of input/output behavior, and once trained, they can be used to simulate system behavior within the space where they were trained.They do this by interpolating specified inputs among the training inputs to yield outputs that are interpolations of =Ming outputs. The reason for using ANNs for the simulation of system response is that they provide accurate approximations of system behavior and are typically much more efficient than phenomenological models. This efficiency is very important in situations where multiple response computations are required, as in, for example, Monte Carlo analysis of probabilistic system response. This paper describes two frameworks in which we have used ANNs to good advantage in the approximate simulation of the behavior of physical system response. These frameworks are the non-recurrent and recurrent frameworks. It is assumed in these applications that physical experiments have been performed to obtain data characterizing the behavior of a system, or that an accurate finite element model has been run to establish system response. The paper provides brief discussions on the operation of ANNs, the operation of two different types of mechanical systems, and approaches to the solution of some special problems that occur in connection with ANN simulation of physical system response. Numerical examples are presented to demonstrate system simulation with ANNs.

Physical Description

21 p.

Notes

OSTI as DE96014025

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  • 30. annual Hawaii international conference on system sciences, Wailea, HI (United States), 7-10 Jan 1997

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  • Other: DE96014025
  • Report No.: SAND--96-1861C
  • Report No.: CONF-970112--2
  • Grant Number: AC04-94AL85000
  • Office of Scientific & Technical Information Report Number: 434369
  • Archival Resource Key: ark:/67531/metadc683142

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  • December 31, 1996

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  • July 25, 2015, 2:20 a.m.

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  • April 14, 2016, 9:08 p.m.

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Benjamin, A.; Altman, B.; O`Gorman, C.; Rodeman, R. & Paez, T.L. Use of artificial neural networks for analysis of complex physical systems, article, December 31, 1996; Albuquerque, New Mexico. (digital.library.unt.edu/ark:/67531/metadc683142/: accessed November 19, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.