192 citations · 453 across the 13 of their papers we have counts for
14 papers
PIFON-EPT: MR-Based Electrical Property Tomography Using Physics-Informed Fourier Networks
Xinling Yu, José E. C. Serrallés, Ilias I. Giannakopoulos +4
We propose Physics-Informed Fourier Networks for Electrical Properties (EP) Tomography (PIFON-EPT), a novel deep learning-based method for EP reconstruction using noisy and/or inco…
MR-Based Electrical Property Reconstruction Using Physics-Informed Neural Networks
Xinling Yu, José E. C. Serrallés, Ilias I. Giannakopoulos +4
Electrical properties (EP), namely permittivity and electric conductivity, dictate the interactions between electromagnetic waves and biological tissue. EP can be potential biomark…
Stochastic Collocation with Non-Gaussian Correlated Parameters via a New Quadrature Rule
Chunfeng Cui, Zheng Zhang
This paper generalizes stochastic collocation methods to handle correlated non-Gaussian random parameters. The key challenge is to perform a multivariate numerical integration in a…
Uncertainty Quantification of Electronic and Photonic ICs with Non-Gaussian Correlated Process Variations
Chunfeng Cui, Zheng Zhang
Since the invention of generalized polynomial chaos in 2002, uncertainty quantification has impacted many engineering fields, including variation-aware design automation of integra…
A Big-Data Approach to Handle Many Process Variations: Tensor Recovery and Applications
Zheng Zhang, Tsui-Wei Weng, Luca Daniel
Fabrication process variations are a major source of yield degradation in the nano-scale design of integrated circuits (IC), microelectromechanical systems (MEMS) and photonic circ…
Tensor Computation: A New Framework for High-Dimensional Problems in EDA
Zheng Zhang, Kim Batselier, Haotian Liu +2
Many critical EDA problems suffer from the curse of dimensionality, i.e. the very fast-scaling computational burden produced by large number of parameters and/or unknown variables.…
A Big-Data Approach to Handle Process Variations: Uncertainty Quantification by Tensor Recovery
Zheng Zhang, Tsui-Wei Weng, Luca Daniel
Stochastic spectral methods have become a popular technique to quantify the uncertainties of nano-scale devices and circuits. They are much more efficient than Monte Carlo for cert…
Probabilistic Power Flow Computation via Low-Rank and Sparse Tensor Recovery
Zheng Zhang, Hung Dinh Nguyen, Konstantin Turitsyn +1
This paper presents a tensor-recovery method to solve probabilistic power flow problems. Our approach generates a high-dimensional and sparse generalized polynomial-chaos expansion…
Stochastic Testing Method for Transistor-Level Uncertainty Quantification Based on Generalized Polynomial Chaos
Zheng Zhang, Tarek A. El-Moselhy, Ibrahim +2
Uncertainties have become a major concern in integrated circuit design. In order to avoid the huge number of repeated simulations in conventional Monte Carlo flows, this paper pres…
Calculation of Generalized Polynomial-Chaos Basis Functions and Gauss Quadrature Rules in Hierarchical Uncertainty Quantification
Zheng Zhang, Tarek A. El-Moselhy, Ibrahim +2
Stochastic spectral methods are efficient techniques for uncertainty quantification. Recently they have shown excellent performance in the statistical analysis of integrated circui…
Efficient Uncertainty Quantification for the Periodic Steady State of Forced and Autonomous Circuits
Zheng Zhang, Tarek A. El-Moselhy, Paolo Maffezzoni +3
This brief paper proposes an uncertainty quantification method for the periodic steady-state (PSS) analysis with both Gaussian and non-Gaussian variations. Our stochastic testing f…
Uncertainty Quantification for Integrated Circuits: Stochastic Spectral Methods
Zheng Zhang, Ibrahim, M. Elfadel +1
Due to significant manufacturing process variations, the performance of integrated circuits (ICs) has become increasingly uncertain. Such uncertainties must be carefully quantified…
Stochastic Testing Simulator for Integrated Circuits and MEMS: Hierarchical and Sparse Techniques
Zheng Zhang, Xiu Yang, Giovanni Marucci +5
Process variations are a major concern in today's chip design since they can significantly degrade chip performance. To predict such degradation, existing circuit and MEMS simulato…
Enabling High-Dimensional Hierarchical Uncertainty Quantification by ANOVA and Tensor-Train Decomposition
Zheng Zhang, Xiu Yang, Ivan V. Oseledets +2
Hierarchical uncertainty quantification can reduce the computational cost of stochastic circuit simulation by employing spectral methods at different levels. This paper presents an…