Stochastic Hybrid Simulation (SHS) requires reliable prediction of structural responses to quantify uncertainties, yet conducting repeated runs via nonlinear time-history analysis or shaking-table testing is computationally or experimentally prohibitive. Although neural networks show promise for nonlinear response prediction, they struggle under the extremely limited high-fidelity data typical of experimental substructures in SHS. While Physics-Informed Neural Networks (PINNs) reduce data demands, they strictly rely on governing equations, which often fail to capture the complex, unmodeled nonlinearities (e.g., friction, noise) inherent in real physical specimens. Consequently, existing methods face challenges in accurately modeling experimental substructures. In this study, a Prior-Knowledge-Infused Neural Network (PKNN) is developed as a high-fidelity surrogate to replace the physical substructure in SHS. A two-stage training strategy is proposed: the network is first pretrained using abundant data from a simplified numerical model to learn fundamental physical laws, and subsequently fine-tuned with scarce shaking-table measurements to correct the physics-based bias and capture complex realistic behaviors. A 1/3-scale steel frame is used to evaluate the approach, predicting maximum inter-story drift ratios under varying structural parameters and ground-motion intensities. Results indicate that, in data-scarce regimes, PKNN maintains significantly higher predictive stability and accuracy than conventional neural networks or polynomial chaos expansions. These findings suggest that embedding simplified physics through pretraining offers a practical means to reduce the demand for high-fidelity data while retaining the ability to model nonlinear response and offers a robust pathway for SHS-oriented experimental design and uncertainty quantification.
Zhang et al. (Sun,) studied this question.