activity
20192021
most citedPower Iteration for Tensor PCA

7 citations · 16 across the 5 of their papers we have counts for

collaborators

11 papers

math.PR20212 cited

Long Random Matrices and Tensor Unfolding

Gérard Ben Arous, Daniel Zhengyu Huang, Jiaoyang Huang

In this paper, we consider the singular values and singular vectors of low rank perturbations of large rectangular random matrices, in the regime the matrix is "long": we allow the…

math.NA20212 cited

Unscented Kalman Inversion: Efficient Gaussian Approximation to the Posterior Distribution

Daniel Z. Huang, Jiaoyang Huang

The unscented Kalman inversion (UKI) method presented in [1] is a general derivative-free approach for the inverse problem. UKI is particularly suitable for inverse problems where…

math.NA20214 cited

Improve Unscented Kalman Inversion With Low-Rank Approximation and Reduced-Order Model

Daniel Z. Huang, Jiaoyang Huang

The unscented Kalman inversion (UKI) presented in [1] is a general derivative-free approach to solving the inverse problem. UKI is particularly suitable for inverse problems where…

math.ST20207 cited

Power Iteration for Tensor PCA

Jiaoyang Huang, Daniel Z. Huang, Qing Yang +1

In this paper, we study the power iteration algorithm for the spiked tensor model, as introduced in [44]. We give necessary and sufficient conditions for the convergence of the pow…

cs.CE2020

A Computationally Tractable Framework for Nonlinear Dynamic Multiscale Modeling of Membrane Fabric

Philip Avery, Daniel Z. Huang, Wanli He +3

A general-purpose computational homogenization framework is proposed for the nonlinear dynamic analysis of membranes exhibiting complex microscale and/or mesoscale heterogeneity ch…

math.NA2020

Learning Constitutive Relations using Symmetric Positive Definite Neural Networks

Kailai Xu, Daniel Z. Huang, Eric Darve

We present the Cholesky-factored symmetric positive definite neural network (SPD-NN) for modeling constitutive relations in dynamical equations. Instead of directly predicting the…