This paper proposes a Physics-Informed Neural Frequency Response Framework for learning and interpreting the frequency-domain behavior of semi-active shunted piezoelectric tuned mass dampers. The motivation is that the behavior of such systems is most naturally expressed through frequency response functions, while the governing electromechanical interactions depend strongly on hidden structural and shunt parameters. Conventional data-driven models can approximate these mappings, but they often lack physical consistency, require large training datasets, and provide limited interpretability. To address these limitations, the proposed framework combines a physics-based forward frequency-response model, a neural inverse learning module, and an explainability component. The forward model is used to generate synthetic complex-valued frequency-response data over a broad range of structural and shunt configurations while preserving the governing electromechanical behavior of the system. Based on synthetic frequency-response data, the neural inverse model is trained to estimate hidden parameters from spectral response signatures and is subsequently evaluated using independently measured experimental FRFs. This synthetic-to-experimental design enables fast parameter inference without solving a new optimization problem for each measured case. To improve robustness to realistic conditions, controlled noise is introduced only at the inverse-training stage, while the underlying physics model remains noise-free. In addition, the learned representation is analyzed through latent-space organization, sensitivity mapping, and reduced symbolic distillation in order to extract interpretable electromechanical response descriptors. The resulting framework provides a data-efficient and explainable ML approach for frequency-response-based identification and inverse tuning of STMD.
A neural calibration framework is presented for estimating the nonlinear hysteretic behavior of jointed structures using the parameters of the Bouc-Wen model, which serves as a reduced-order representation of bolted joints under dynamic excitation. This methodology integrates data-driven modeling, physics-based representation, and ensemble-based variability analysis to achieve robust parameter estimation and to propagate parameter dispersion into response-level uncertainty envelopes. A feed-forward neural network is employed to minimize the discrepancy between simulated and measured responses, with multiple random initializations producing an ensemble of calibrations. This ensemble quantifies variability resulting from both neural initialization and experimental repetitions under identical boundary conditions. Experimental validation was performed on a beam testbed subjected to different vibration regimes and various torque-tightening levels. The results demonstrate that the calibrated models capture key hysteretic features, such as stiffness degradation and energy dissipation, while yielding physically admissible parameter sets and consistent uncertainty envelopes across repeated measurements. The evolution of these parameters may serve as health indicators for structural health monitoring (SHM), facilitating the development of uncertainty-aware digital shadows for nonlinear structural systems.
Estevão Fuzaro de Almeida, G. Chevallier, Samuel da Silva· e-Journal of Nondestructive...· 0 citations
This study develops a unified physics-constrained deep reinforcement learning framework for OpenSees Steel02 and DowelType identification, giving accuracy comparable with tuned PSO at the same online OpenSees-call budget while retaining a reusable learned initialization step.
Conventional integer-order models do not adequately represent intrinsic memory effects in the dynamics of modern power systems, which operate under increasingly nonlinear and uncertain conditions. This work proposes a Fractional-Order Physics-informed Neural Network (FOPINN) framework for power system stability analysis and forward dynamic modelling. The method allows for an accurate representation of memory-dependent dynamics by directly integrating fractional-order swing equations into the learning process. The framework is extended to a reduced Multi-Machine Infinite Bus (MMIB) system to examine inter-machine coupling and synchronisation dynamics, following with a validation on a Single Machine Infinite Bus (SMIB) system. A comparison with integer-order PINNs, Caputo-L1 fractional solvers, and classical RK4 shows that FOPINN achieves significantly improved prediction accuracy while maintaining physical consistency. The trained FOPINN further provides approximately $193\times $ faster inference than repeated Caputo–L1 numerical computation while demonstrating the expected influence of fractional order, damping, inertia, and network coupling on transient stability. The results show that transient behaviour is strongly influenced by fractional-order dynamics. Stronger memory effects alter the response as the fractional order decreases, causing long-lasting transient deviations and slower convergence before the system reaches steady state. It is demonstrated that memory effects propagate via machine coupling in the MMIB system, altering synchronisation characteristics and the stability margin as the disturbance level increases. Fractional-order analysis reveals oscillatory or unstable responses in high-loading regimes, whereas classical integer-order models predict stable behaviour. These results establish FOPINN as an effective, reusable and physics-consistent framework for capturing memory-driven dynamics in contemporary power grids and emphasise the significance of integrating fractional-order modeling for realistic power system analysis.
V. S. Malavika, E. Gopalakrishnan, K. Chandan et al.· IEEE Access· 0 citations
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