Researchers at the U.S. Department of Energy's Princeton Plasma Physics Laboratory (PPPL) and Princeton University are employing artificial intelligence to improve predictive capability.
Today, researchers at the U.S. Department of Energy's (DOE Princeton Plasma Physics Laboratoryy (PPPL) and Princeton University are employing artificial intelligence to improve predictive capability.
However, constraining donor target parameter allocation by similar catchment area did not improve predictive capability in ungauged catchments.
In order to improve predictive capability by testing and validating models of turbulent-transport, detailed measurements of fluctuations in high-performance, reactor relevant tokamak plasmas are required.
The model-predicted FCG results were compared with the experimental FCG data of GH4169 Ni-based superalloy, demonstrating improved predictive capability than the classical N-R model.
In particular, the proposed model exhibited improved predictive capability in comparison to published zero-dimensional diesel engine autoignition correlations that are typically employed in bulk parameter, diesel engine performance simulation models.
The model is validated with data and shows a significantly improved predictive capability (AAD = 12.8% for all ammonia data) compared to models and correlation developed for different classes of fluids and channel geometries.
The prediction methods proposed and developed in this study based on sample entropy and fractal structure over HR, SBP, DBP, and SV, individually, jointly, or by pre-selection with principal component analysis, with or without classification SVM, did not improve predictive capability compared with the application of SVM classifier on the preceding methods separately.
In order to improve predictive capabilities, attempts at clinical prediction rules using more than one clinical factor have been established to help to better determine a likelihood of PE [ 43].
Studying pedestal physics in collaboration with international tokamak experiments to improve predictive capabilities of plasma mode transitions in magnetic-confinement fusion devices.
