Data for uncertainty quantification via adaptive ANN

Data for uncertainty quantification via adaptive ANN

Citation Author(s):
Runze
Hu
Submitted by:
Runze Hu
Last updated:
Wed, 04/24/2019 - 04:30
DOI:
10.21227/hhnh-s530
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Abstract: 

The Monte Carlo method (MCM) is known as the gold standard technique for uncertainty quantification (UQ). However, it requires a considerable number of simulations leading to excessive computational resources. Though some UQ techniques such as the non-intrusive polynomial chaos (NIPC) expansion method are ideal alternatives to MCM, the computational cost remains unaffordable when systems consist of a high number of random variables. This paper proposes an adaptive artificial neural network (ANN) aiming to replace thousands of system-runs required in the MCM, thereby improving computational efficiency of the MCM. We design an activation function with which an ANN learns from the data to the greatest extent. Furthermore, in order to improve the accuracy of the estimation of the system output via ANN, methods for hyperparameter optimisation and a series of termination criteria of ANN are proposed based on the leave-one-out cross-validation method. In the application of the finite difference time domain computation, the proposed method achieves up to 13 times speedup and 20\% improvement of the accuracy of the UQ estimation compared to the traditional NIPC expansion method. 

Instructions: 

Data files corresponding to the figures in the manuscript of "An adaptive artificial neural network for uncertainty quantification of FDTD computation in human body"

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[1] Runze Hu, "Data for uncertainty quantification via adaptive ANN", IEEE Dataport, 2019. [Online]. Available: http://dx.doi.org/10.21227/hhnh-s530. Accessed: Jul. 22, 2019.
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doi = {10.21227/hhnh-s530},
url = {http://dx.doi.org/10.21227/hhnh-s530},
author = {Runze Hu },
publisher = {IEEE Dataport},
title = {Data for uncertainty quantification via adaptive ANN},
year = {2019} }
TY - DATA
T1 - Data for uncertainty quantification via adaptive ANN
AU - Runze Hu
PY - 2019
PB - IEEE Dataport
UR - 10.21227/hhnh-s530
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Runze Hu. (2019). Data for uncertainty quantification via adaptive ANN. IEEE Dataport. http://dx.doi.org/10.21227/hhnh-s530
Runze Hu, 2019. Data for uncertainty quantification via adaptive ANN. Available at: http://dx.doi.org/10.21227/hhnh-s530.
Runze Hu. (2019). "Data for uncertainty quantification via adaptive ANN." Web.
1. Runze Hu. Data for uncertainty quantification via adaptive ANN [Internet]. IEEE Dataport; 2019. Available from : http://dx.doi.org/10.21227/hhnh-s530
Runze Hu. "Data for uncertainty quantification via adaptive ANN." doi: 10.21227/hhnh-s530