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- W1663396228 abstract "Energy confinement data of large fusion devices are analyzed in terms of reduced variables which consist of certain combinations of the machine variables. The goal is to predict the single variable behaviour from a data set with entries which differ in more than one variable setting from each other. Bayesian neural networks are used to model the hyper‐surface evolving for the energy confinement as a function of the reduced variables. In order to tell which neural net is best and to provide expectation values the multi‐dimensional multi‐modal marginalization integrals are calculated employing a Monte Carlo method called perfect tempering." @default.
- W1663396228 created "2016-06-24" @default.
- W1663396228 creator A5053370786 @default.
- W1663396228 date "2004-01-01" @default.
- W1663396228 modified "2023-09-25" @default.
- W1663396228 title "Modeling Energy Confinement in Plasma Devices by Neural Networks" @default.
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- W1663396228 doi "https://doi.org/10.1063/1.1835234" @default.
- W1663396228 hasPublicationYear "2004" @default.
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