Resonance-Anchored Residual Surrogate for Spectral Prediction and Inverse Design of Multiple Terahertz Metamaterial Absorber Topologies

Abstract

Efficient forward prediction and inverse design can bypass the complexity and computational cost of traditional full-wave simulations, accelerating the development of terahertz metamaterial absorbers. Most current machine-learning approaches for spectral prediction or inverse design are limited to absorbers with a single topology, requiring retraining whenever the topology changes. Simultaneous forward prediction and inverse design also require separate models that are trained independently. This study proposes a resonance-anchored residual surrogate framework for resonance-frequency prediction and goal-oriented inverse design across multiple absorber topologies. Unlike conventional spectral prediction, the proposed spectral representation consists of a resonance frequency and a continuous local absorption spectrum centered on the resonance peak. The residual network is trained using a physics-guided dynamic anchor loss to improve the accuracy of resonance-frequency prediction, local spectral reconstruction, peak alignment, and absorptivity estimation. On the test set, the model achieves a normalized resonance-frequency MSE of 6.64 × 10−5, a normalized spectral-waveform MSE of 3.58 × 10−3, and a physical-domain absorptivity RMSE of 0.0054. Ablation experiments verify the effectiveness of the residual connections and the dynamic anchor loss. The trained surrogate is then frozen and integrated into a projected gradient descent loop for inverse design. Full-wave simulations demonstrate that the optimized structures successfully reproduce the target resonance responses within the sampled design space.