| Modeling Energy Confinement in Plasma (2008) | |||||||||||||
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. | |||||||||||||
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