Algorithms for Wave Scattering of Random Media: Fast multipole method in layered media and a phase shift deep neural network for wideband learning
Date/Time:21 May 2019 10:00
Venue: S17 #04-06 SR1
Speaker: Wei Cai, Southern Methodist University
Algorithms for Wave Scattering of Random Media: Fast multipole method in layered media and a phase shift deep neural network for wideband learning
In this talk, we will present two algorithms and numerical results for solving electromagnetic wave scattering of random meta-materials. Firstly, a fast multipole method for 3-D Helmholtz equation for layered media will be presented based on new multipole expansion (ME) and multipole to local translation (M2L) operators for layered media Green’s functions. Secondly, a parallel phase shift deep neural network (PhaseDNN) is proposed for wideband data learning. In order to achieve uniform convergence for low to high frequency content of data, phase shifts are used to convert high frequency learning to low frequency learning. Due to the fast learning of many DNNs in the low frequency range, PhaseDNN is able to learn wideband data uniformly in all frequencies.
Reference:
[1] B.Wang, W.Z. Zhang, W. Cai, Fast Multipole Method For 3-D Helmholtz Equation In Layered Media, arXiv:1902.05132
[2] W. Cai, X.G. Li, L.Z. Liu, PhaseDNN – A Parallel Phase Shift Deep Neural Network for Adaptive Wideband Learning , arXiv:1905.01389
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