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

Author

Listed:
  • Xi Cheng

    (Chaire C2M, LTCI, Télécom Paris, 19 Place Marguerite Perey, 91120 Palaiseau, France)

  • Clément Henry

    (Department of Electronics and Telecommunications, Politecnico di Torino, IT-10129 Turin, Italy)

  • Francesco P. Andriulli

    (Department of Electronics and Telecommunications, Politecnico di Torino, IT-10129 Turin, Italy)

  • Christian Person

    (IMT Atlantique/Lab-STICC UMR CNRS 6285, Technopole Brest Iroise-CS83818-29238, 29238 Brest CEDEX 03, France)

  • Joe Wiart

    (Chaire C2M, LTCI, Télécom Paris, 19 Place Marguerite Perey, 91120 Palaiseau, France)

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.

Suggested Citation

  • Xi Cheng & Clément Henry & Francesco P. Andriulli & Christian Person & Joe Wiart, 2020. "A Surrogate Model Based on Artificial Neural Network for RF Radiation Modelling with High-Dimensional Data," IJERPH, MDPI, vol. 17(7), pages 1-11, April.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:7:p:2586-:d:343589
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