In many situations across computational science and engineering, multiple computational
models are available that describe a system of interest. These different models have varying …

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 …

This paper introduces an innovative physics-informed deep learning framework for
metamodeling of nonlinear structural systems with scarce data. The basic concept is to …

This paper formulates a model order reduction method for electromagnetic boundary
element analysis and extends it to computer-aided design integrated shape optimization of …

Accurate prediction of building's response subjected to earthquakes makes possible to
evaluate building performance. To this end, we leverage the recent advances in deep …

We propose a thick-restart block Lanczos method, which is an extension of the thick-restart
Lanczos method with the block algorithm, as an eigensolver of the large-scale shell-model …

We discuss the modelling framework of port-Hamiltonian descriptor systems and their use in
numerical simulation and control. The structure is ideal for automated network-based …

A dimension reduction method called discrete empirical interpolation is proposed and
shown to dramatically reduce the computational complexity of the popular proper orthogonal …

In this article, an innovative method is proposed for broadband topology optimization of
sound‐absorbing materials adhering to the surface of a sound barrier structure. Helmholtz …

In today's technological world, physical and artificial processes are mainly described by
mathematical models, which can be used for simulation or control. These processes are …