50 citations · 72 across the 6 of their papers we have counts for
6 papers
Physics-Driven Neural Network for Solving Electromagnetic Inverse Scattering Problems
Yutong Du, Zicheng Liu, Bazargul Matkerim +4
In recent years, deep learning-based methods have been proposed for solving inverse scattering problems (ISPs), but most of them heavily rely on data and suffer from limited genera…
An Intriguing Property of Geophysics Inversion
Yinan Feng, Yinpeng Chen, Shihang Feng +3
Inversion techniques are widely used to reconstruct subsurface physical properties (e.g., velocity, conductivity) from surface-based geophysical measurements (e.g., seismic, electr…
Physics-guided Loss Functions Improve Deep Learning Performance in Inverse Scattering
Zicheng Liu, Mayank Roy, Dilip K. Prasad +1
Solving electromagnetic inverse scattering problems (ISPs) is challenging due to the intrinsic nonlinearity, ill-posedness, and expensive computational cost. Recently, deep neural…
Silicon substrate significantly alters dipole-dipole resolution in coherent microscope
Zicheng Liu, Krishna Agarwal
Influences of a substrate below samples in imaging performances are studied by reaching the solution to the dyadic Green's function, where the substrate is modeled as half space in…
Surrogate modeling of indoor down-link human exposure based on sparse polynomial chaos expansion
Zicheng Liu, Dominique Lesselier, Bruno Sudret +1
Human exposure induced by wireless communication systems increasingly draws the public attention. Here, an indoor down-link scenario is concerned and the exposure level is statisti…
Surrogate modeling based on resampled polynomial chaos expansions
Z. Liu, D. Lesselier, B. Sudret +1
In surrogate modeling, polynomial chaos expansion (PCE) is popularly utilized to represent the random model responses, which are computationally expensive and usually obtained by d…