Tensor computation: A new framework for high-dimensional problems in EDA
Many critical electronic design automation (EDA) problems suffer from the curse of
dimensionality, ie, the very fast-scaling computational burden produced by large number of …
dimensionality, ie, the very fast-scaling computational burden produced by large number of …
Data-driven modeling of weakly nonlinear circuits via generalized transfer function approximation
This paper presents an extension of the Vector Fitting algorithm with the purpose of
constructing compact behavioral models of weakly nonlinear circuits starting from frequency …
constructing compact behavioral models of weakly nonlinear circuits starting from frequency …
Tensor methods for generating compact uncertainty quantification and deep learning models
Tensor methods have become a promising tool to solve high-dimensional problems in the
big data era. By exploiting possible low-rank tensor factorization, many high-dimensional …
big data era. By exploiting possible low-rank tensor factorization, many high-dimensional …
A tensor-based Volterra series black-box nonlinear system identification and simulation framework
Tensors are a multi-linear generalization of matrices to their d-way counterparts, and are
receiving intense interest recently due to their natural representation of high-dimensional …
receiving intense interest recently due to their natural representation of high-dimensional …