Physics-Informed Deep Learning for Dynamic Model Updating of Smart Viscoelastic Composite Structures
Keywords:
physics-informed deep learning, model updating, viscoelastic composites, structural health monitoring, digital twin, smart infrastructureAbstract
The integration of viscoelastic damping layers into smart composite structures is increasingly deployed in large-scale civil, aerospace, and mechanical infrastructures to mitigate vibration and enhance operational safety. The accurate dynamic characterization of these structures demands model updating procedures that can reconcile time-evolving sensor data with physical governing laws, especially as material degradation, temperature effects, and nonlinear damping phenomena introduce systematic deviations from baseline finite element representations. Physics-informed deep learning offers a unified framework that embeds known constitutive and equilibrium constraints directly into neural network training, enabling dynamic model updating without relying on purely data-driven correlations. This paper presents a system-level examination of physics-informed deep learning architectures for dynamic model updating of smart viscoelastic composite structures, focusing on architectural trade-offs, computational scalability, governance, and deployment across heterogeneous sensing networks. Through an interdisciplinary lens that bridges structural mechanics, artificial intelligence, and infrastructure policy, the discussion evaluates how physics-informed loss formulations, distributed sensor integration, and federated learning protocols can transform model updating into a continuous, trustworthy digital twin service. Emphasis is placed on structural resilience against adversarial sensor noise, fairness in risk-aware decision-making, sustainability of embedded computing resources, and the policy frameworks required to certify physics-informed digital twins for safety-critical applications. The analysis reveals that the principal challenges reside not in algorithmic innovation alone but in designing institutional, contractual, and computational architectures that ensure long-term robustness, data provenance, and equitable access to model updating capabilities across public infrastructure portfolios.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
