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A Surrogate Model Based on Artificial Neural Network for RF Radiation Modelling with High-Dimensional Data

Abstract : This paper focuses on quantifying the uncertainty in the specific absorption rate values of the brain induced by the uncertain positions of the electroencephalography electrodes placed on the 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.
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https://hal.telecom-paris.fr/hal-02887137
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Submitted on : Thursday, July 2, 2020 - 7:52:44 AM
Last modification on : Saturday, October 10, 2020 - 10:37:08 AM
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Xi Cheng, Clément Henry, Francesco Andriulli, Christian Person, Joe Wiart. A Surrogate Model Based on Artificial Neural Network for RF Radiation Modelling with High-Dimensional Data. International Journal of Environmental Research and Public Health, MDPI, 2020, ⟨10.3390/ijerph17072586⟩. ⟨hal-02887137⟩

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