Cheng, X.; Henry, C.; Andriulli, F.P.P.; Person, C.; Wiart, J. A Surrogate Model Based on Artificial Neural
Network for RF Radiation Modelling with
High-Dimensional Data. Int. J. Environ. Res. Public Health2020, 17, 2586.
Cheng, X.; Henry, C.; Andriulli, F.P.P.; Person, C.; Wiart, J. A Surrogate Model Based on Artificial Neural
Network for RF Radiation Modelling with
High-Dimensional Data. Int. J. Environ. Res. Public Health 2020, 17, 2586.
Cheng, X.; Henry, C.; Andriulli, F.P.P.; Person, C.; Wiart, J. A Surrogate Model Based on Artificial Neural
Network for RF Radiation Modelling with
High-Dimensional Data. Int. J. Environ. Res. Public Health2020, 17, 2586.
Cheng, X.; Henry, C.; Andriulli, F.P.P.; Person, C.; Wiart, J. A Surrogate Model Based on Artificial Neural
Network for RF Radiation Modelling with
High-Dimensional Data. Int. J. Environ. Res. Public Health 2020, 17, 2586.
Abstract
This paper focuses on quantifying the uncertainty in the specific absorption rate values of brain induced by the uncertain positions of the electroencephalography electrodes placed on patient's scalp. To avoid running a large number of simulations, an artificial neural network architecture for uncertainty quantification involving high-dimensional data is proposed in this paper. The proposed method is demonstrated to be an attractive alternative to conventional uncertainty quantification methods because of its considerable advantage in the computational expense and speed.
Keywords
artificial neural networks; uncertainty quantification; specific absorption rate
Subject
Computer Science and Mathematics, Mathematics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
