v1.0.0 – Journal Publication Release (Data-Centric Engineering, 2024)

PINNs_FOR_SHM – Version 1.0.0

This release corresponds to the exact implementation used in the journal publication:

Al-Adly AIF, Kripakaran P. (2024).
Physics-informed neural networks for structural health monitoring:
a case study for Kirchhoff–Love plates.
Data-Centric Engineering, 5, e6.
https://doi.org/10.1017/dce.2024.4

Description

This repository contains the implementation of Physics-Informed Neural Networks (PINNs)
for Structural Health Monitoring (SHM) applied to Kirchhoff–Love plate theory.

The framework includes:

• Governing equation formulation for Kirchhoff–Love plates
• PINN architecture implementation
• Training procedures
• Post-processing scripts for result visualisation

Reproducibility

This version reproduces the numerical experiments and results presented in the paper.

Citation

If you use this code, please cite the above paper. A Zenodo DOI for this
software version will be added upon archival.