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<p>Digital twins are increasingly used as tools for managing engineering systems through continuous synchronization between physical assets and their virtual counterparts, enabling improved monitoring, prediction and control throughout the system lifecycle. Maintaining accurate alignment requires reliable state estimation from sparse sensor measurements along with adaptive calibration of model parameters to account for degradation, environmental variability and operational uncertainties. Physics informed neural networks address these requirements by embedding governing equations with observational data within a unified optimization framework that supports simultaneous state reconstruction and parameter identification while preserving physical consistency. This review examines the current state of the art in PINN based digital twin synchronization and model updating, presents the mathematical foundations that connect PINNs to classical collocation methods and describes how automatic differentiation enables efficient enforcement of physical constraints. Applications spanning manufacturing, aerospace, energy and infrastructure demonstrate that PINN based digital twins achieve high diagnostic accuracy, low inference latency and significant reductions in operational cost. Key challenges that remain include parameter identifiability, scalability limitations arising from the curse of dimensionality, real time deployment constraints, gaps in validation methodology and robustness to distribution shift.</p>

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Digital Twin Engineering with Physics-Informed Neural Networks: State of the Art and Open Challenges

  • 期刊论文

  • 发表日期：

    2026年8月3日

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  • Naveen Raj Rajamani,
  • Santo Banerjee

摘要

Digital twins are increasingly used as tools for managing engineering systems through continuous synchronization between physical assets and their virtual counterparts, enabling improved monitoring, prediction and control throughout the system lifecycle. Maintaining accurate alignment requires reliable state estimation from sparse sensor measurements along with adaptive calibration of model parameters to account for degradation, environmental variability and operational uncertainties. Physics informed neural networks address these requirements by embedding governing equations with observational data within a unified optimization framework that supports simultaneous state reconstruction and parameter identification while preserving physical consistency. This review examines the current state of the art in PINN based digital twin synchronization and model updating, presents the mathematical foundations that connect PINNs to classical collocation methods and describes how automatic differentiation enables efficient enforcement of physical constraints. Applications spanning manufacturing, aerospace, energy and infrastructure demonstrate that PINN based digital twins achieve high diagnostic accuracy, low inference latency and significant reductions in operational cost. Key challenges that remain include parameter identifiability, scalability limitations arising from the curse of dimensionality, real time deployment constraints, gaps in validation methodology and robustness to distribution shift.