Although nonnegative matrix factorization (NMF) is widely used, some matrix factorization
methods result in misleading results and waste of computing resources due to lack of timely …

Corrosion under insulations is described as localized corrosion that forms because of
moisture penetration through the insulation materials or due to contaminants' presence …

Nonnegative matrix factorization (NMF) has become a widely used tool for the analysis of
high-dimensional data as it automatically extracts sparse and meaningful features from a set …

Sparse principal component analysis (SPCA) has emerged as a powerful technique for
modern data analysis, providing improved interpretation of low-rank structures by identifying …

Identifying the underlying structure of a data set and extracting meaningful information is a
key problem in data analysis. Simple and powerful methods to achieve this goal are linear …

Randomized numerical linear algebra-RandNLA, for short-concerns the use of
randomization as a resource to develop improved algorithms for large-scale linear algebra …

Matrix decompositions are fundamental tools in the area of applied mathematics, statistical
computing, and machine learning. In particular, low-rank matrix decompositions are vital …

We propose a scientific machine learning (SciML) algorithm toward 3-D dynamic digital
twins, which represent multiphysics coupling effects in large-scale electronic chips. The …

We propose a general framework to accelerate significantly the algorithms for nonnegative
matrix factorization (NMF). This framework is inspired from the extrapolation scheme used to …

Projection‐based nonlinear model order reduction (MOR) methods typically make use of a
reduced basis to approximate high‐dimensional quantities. However, the most popular …