Spectrum prediction improvement with phenomenological statistical learning

Statistical learning can be used for spectrum prediction, significantly reducing the large computational resources required by simulation software. However, traditional statistical learning methods face challenges such as interpretability and issues with prediction accuracy. By combining phenomenological theory with statistical learning, we propose a resonance-informed model that improves the accuracy and interpretability of spectrum predictions.