Predicting chaotic time-series with wavelet networks

A new technique, wavelet network, is introduced to predict chaotic time series. By using this technique, firstly, we make accurate short-term predictions of the time series from chaotic attractors. Secondly, we make accurate predictions of the values and bifurcation structures of the time series from dynamical systems whose parameter values are changing with time. Finally we predict chaotic attractors by making long-term predictions based on remarkably few data points, where the correlation dimensions of predicted attractors are calculated and are found to be almost identical to those of actual attractors.

Ablation of hydrogen isotopic spherical pellet due to the impact of energetic ions of the respective isotopes

The ablation rate of a hydrogen isotopic spherical pellet G(is) due to the impact of energetic ions of the respective isotopes and its scaling law are obtained using the transsonic neutral-shielding model, where subscript s might refer to either hydrogen or deuterium. Numerical results show that if E0s/E0e2 greater-than-or-equal-to 1.5, G(is)/G(es) greater-than-or-equal-to 20%, where E0s and E0e are the energy of undisturbed ion and electron, respectively, and G(es) is the ablation rate of a pellet due to the impact of electrons. Hence, under the NBI heating, the effect of the impact of energetic ions on the pellet ablation should be taken into consideration. This result also gives an explanation of the observed enhancement of pellet ablation during NBIH.