Structural parameter identification with hybrid physics informed neural network
System identification (SI) is critical for ensuring the reliability of structural and mechanical components across engineering applications. Traditional model-based SI methods often struggle with complex dynamics and the scarcity of accurate physical models, while purely data-driven, model-free approaches though simple and fast lack physical interpretability and suffer from poor generalization. Recent advances such as physics-informed neural networks (PINNs) combine data and physics to overcome these limitations and have shown strong promise for structural health monitoring (SHM). However, most existing methods still require knowledge of the input force, which limits their practical use for parameter estimation. To address this challenge, an input-robust hybrid physics informed neural network (rHPINN) framework is proposed that integrates physics-based system dynamics with the temporal learning capability of HPINN. An output-injection strategy enables rejection of unknown input forces, allowing accurate estimation of system states and spatial health parameters without input force measurements. By explicitly preserving temporal dependencies often overlooked in conventional PINNs the method achieves stable, physics-consistent system identification. Numerical simulations on systems demonstrate that rHPINN remains robust under unknown excitation, measurement noise, sparse data, and varying damage scenarios, highlighting its potential for real-world SHM under uncertain conditions.