This article provides a comprehensive review of deep learning-based structural health
monitoring (DL-based SHM). It encompasses a broad spectrum of DL theories and …

Physics-informed neural networks (PINNs) represent a significant advancement at the
intersection of machine learning and physical sciences, offering a powerful framework for …

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 …

Relying on sufficient training data, the existing fault diagnosis methods rarely focus on the
methodological interpretability and the data scarcity in real industrial scenarios …

N. Wan, M. Huang & Y. Lei under high uncertainties, which means the introduction of the
physics-based loss function significantly enhances the parameters updating ability of the …

Reduced Order Models (ROMs) form essential tools across engineering domains by virtue of
their function as surrogates for computationally intensive digital twinning simulators …

Continuum solid mechanics form the foundation of numerous theoretical studies and
engineering applications. Distinguished from traditional mesh-based numerical solutions …

Abstract Reduced Order Models (ROMs) form essential tools across engineering domains
by virtue of their function as surrogates for computationally intensive digital twinning …

This study introduces an innovative approach that employs Physics-Informed Neural
Networks (PINNs) to address inverse problems in structural analysis. Specifically, this …

Existing studies that use neural networks for civil engineering problems mostly established
neural network models for the entire structure, and seldomly created dynamic interaction …