The present paper describes a method to enhance the capability of, or to broaden the scope
of computational mechanics by using deep learning, which is one of the machine learning …

In this article, we describe a non‐intrusive reduction method for porous media multiphase
flows using Smolyak sparse grids. This is the first attempt at applying such an non‐intrusive …

In this paper, a data-driven structural health monitoring (SHM) approach is proposed to
conduct in situ evaluation of the structural health state, ie, damage location and extent, in …

This paper proposes a data-to-decisions framework—a methodology and a computational
strategy—to assist real-time decisions associated with structural monitoring and informed by …

An accelerated least-squares approach is introduced in this work by incorporating a greedy
point selection method with randomized singular value decomposition (rSVD) to reduce the …

This paper presents a non‐intrusive reduced order model for general, dynamic partial
differential equations. Based upon proper orthogonal decomposition (POD) and Smolyak …

Image-based techniques have been extensively deployed in the fields of condition
assessment and structural mechanics to measure surface effects such as displacements or …

This paper presents an approach for computationally efficient inverse material
characterization using Physics-Informed Neural Networks (PINNs) based on partial-field …

We present a non-destructive approach to sense inclusion objects embedded in a solid
medium remotely from force sensors applied to the medium and boundary displacements …

A novel inversion method is proposed for the identification of cavity shape by combining
Gappy proper orthogonal decomposition (POD) with a direct inversion scheme. Firstly, the …