The rapid growth of deep learning research, including within the field of computational
mechanics, has resulted in an extensive and diverse body of literature. To help researchers …

A parameter identification framework has been developed based on physics-informed
neural networks (PINNs). Physical constraints are taken into account during the training …

In this paper, we address the effective and robust identification of material behavior
parameters from full-field measurements obtained by means of the advanced Digital Image …

The Guadalquivir bridge is a large-scale twin steel truss bridge located in Spain that opened
to traffic in 1929. Since the bridge has come into operation for a long time, structural health …

This paper presents a robust model updating strategy for correcting finite element models
from datasets acquired in low-frequency dynamics. The proposed methodology is based on …

This article proposes a new approach to train physics‐augmented neural networks with
observable data to represent mechanical constitutive laws. To train the neural network and …

Prior to any numerical development, the paper objective is to answer first to a fundamental
question: what is the mathematical form of the most general data-driven constitutive model …

This article proposes a consistent and general approach to train physics-augmented neural
networks with observable data to enrich and represent nonlinear history-dependent material …

This paper presents a novel Optimal Sensor Placement (OSP) strategy that is dedicated to
model updating problems based on the modified Constitutive Relation Error (mCRE) …

The rapid growth of deep learning research, including within the field of computational
mechanics, has resulted in an extensive and diverse body of literature. To help researchers …