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An Uncertainty Analysis on Finite Difference Time-Domain Computations with Artificial Neural Networks: Improving accuracy while maintaining low computational costs
Runze Hu
, Vikass Monebhurrun, Ryutaro Himeno, Hideo Yokota, Fumie Costen
Tsinghua University
CentraleSupélec
Juntendo University
RIKEN
University of Manchester
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Computer Science
Computational Cost
100%
Artificial Neural Network
100%
finite difference time domain
100%
Finite-Difference Time-Domain (FDTD)
100%
Traditional Method
20%
Intensive Computation
20%
Speed-up
20%
Considerable Number
20%
Engineering
Computational Cost
100%
Time Domain
100%
Artificial Neural Network
100%
Uncertainty Quantification
80%
Surrogate Model
40%
Larger Quantity
20%
Material Science
Finite Difference
100%