The application of multi-objective optimization algorithms in the field of structural damage
identification has gained increasing attention in the past few years. Nevertheless, their …

The spatial incompleteness and uncertainties of measured data are unavoidable factors that
could significantly affect the reliability of damage detection results. It is therefore essential to …

Structural damage detection is a well‐known engineering inverse problem in which the
extracting of damage information from the dynamic responses of the structure is considered …

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

An approach for computationally efficient inverse material characterization from partial-field
response measurements that combines the Gappy proper orthogonal decomposition (POD) …

Rubber-like materials are widely deployed in industry because of their outstanding
properties of elasticity and energy dissipation capacity. In order to analyze and improve the …

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

Structural parameter identification often requires an estimate, at least in a qualitative fashion,
of the uncertainty of the solution. This uncertainty quantification should account for the …

One of the important goals of structural health monitoring is to identify structural damage
using measured responses. However, such damage identification is sensitive to noises in …

A generally applicable algorithm for the iterative generation of data ensembles to efficiently
create accurate computational mechanics reduced-order models (ROMs) for use in …