Numerical simulation of large-scale dynamical systems plays a fundamental role in studying
a wide range of complex physical phenomena; however, the inherent large-scale nature of …

Modern applications in engineering and data science are increasingly based on
multidimensional data of exceedingly high volume, variety, and structural richness …

Parameter studies are everywhere in computational science. Complex engineering
simulations must run several times with different inputs to effectively study the relationships …

This work presents a nonintrusive projection-based model reduction approach for full
models based on time-dependent partial differential equations. Projection-based model …

Machine learning and data mining algorithms are becoming increasingly important in
analyzing large volume, multi-relational and multi--modal datasets, which are often …

This survey gives a comprehensive overview of tensor techniques and applications in
machine learning. Tensor represents higher order statistics. Nowadays, many applications …

Deep operator networks (DeepONets) are powerful and flexible architectures that are
attracting attention in multiple fields due to their utility for fast and accurate emulation of …

Proper Orthogonal Decomposition (POD) is a widely used technique for the construction of
low-dimensional approximation spaces from high-dimensional input data. For large-scale …

The QR factorization and the SVD are two fundamental matrix decompositions with
applications throughout scientific computing and data analysis. For matrices with many more …

This paper presents a non-intrusive model order reduction (MOR) for the solution of
parameterized electromagnetic scattering problems, which needs to prepare a database …