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October 20, 2025

A Framework for Enhancing Self-Generating Digital Twins with Hierarchical Models, Physics-Informed Machine Learning, Gen-AI, and Dynamic Diffusion Models for Real-Time SHM and Predictive Maintenance

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Authors

SASasan Darius Adib

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Overview

Framework integrates hierarchical digital twins and physics-informed machine learning for improved real-time SHM, suggesting efficiencies in predictive maintenance.

Key Points

  • The proposed framework enhances self-generating digital twins by improving predictive accuracy through hierarchical models.
  • Integrating physics-informed machine learning facilitates better predictive maintenance by applying fundamental physical laws.
  • Dynamic diffusion models support precise damage predictions based on varying environmental conditions and operational states.
  • Overall, the framework aims to boost efficiency in infrastructure management while promoting sustainability.

Cite This Study

Sasan Darius Adib (2025) studied this question.

synapsesocial.com/papers/68f5fcd68d54a28a75cf1fdahttps://doi.org/10.12783/shm2025/37550
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