Actions for Using neutral network to infer the hydrodynamic yield of aspherical sources [electronic resource].

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Using neutral network to infer the hydrodynamic yield of aspherical sources [electronic resource].

Published
Washington, D.C. : United States. Dept. of Energy, 1993.
Oak Ridge, Tenn. : Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy.
Physical Description
9 pages : digital, PDF file
Additional Creators
Lawrence Livermore National Laboratory, United States. Department of Energy, and United States. Department of Energy. Office of Scientific and Technical Information
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Summary
We distinguish two kinds of difficulties with yield determination from aspherical sources. The first kind, the spoofing difficulty, occurs when a fraction of the energy of the explosion is channeled in such a way that it is not detected by the CORRTEX cable. In this case, neither neural networks nor any expert system can be expected to accurately estimate the yield without detailed information about device emplacement within the canister. Numerical simulations however, can provide an upper bound on the undetected fraction of the explosive energy. In the second instance, the interpretation difficulty, the data appear abnormal when analyzed using similar-explosion-scaling and the assumption of a spherical front. The inferred yield varies with time and the confidence in the yield estimate decreases. It is this kind of problem we address in this paper and for which neural networks can make a contribution. We used a back propagation neural network to infer the hydrodynamic yield of simulated aspherical sources. We trained the network using a subset of simulations from 3 different aspherical sources, with 3 different yields, and 3 satellite offset separations. The trained network was able to predict the yield within 15% in all cases and to identify the correct type of aspherical source in most cases. The predictive capability of the network increased with a larger training set. The neural network approach can easily incorporate information from new calculations or experiments and is therefore flexible and easy to maintain. We describe the potential capabilities and limitations in using such networks for yield estimations.
Report Numbers
E 1.99:ucrl-jc--112878
E 1.99: conf-930397--10
conf-930397--10
ucrl-jc--112878
Subject(s)
Other Subject(s)
Note
Published through SciTech Connect.
07/01/1993.
"ucrl-jc--112878"
" conf-930397--10"
"DE94001525"
Numerical modeling for underground nuclear test monitoring symposium,Durango, CO (United States),23-25 Mar 1993.
Glenn, L.A.; Moran, B.
Funding Information
W-7405-ENG-48
View MARC record | catkey: 14358787