Inverse Modeling for Artifact Removal in Photonic Data: A Computational Physics and Transfer Learning-Based Approach

Etaloning artifacts introduce notable distortions in spectroscopic data, complicating downstream analysis and interpretation. We present an inverse modeling framework that integrates computational physics with deep learning to address this challenge. Our approach employs a two-phase transfer learning strategy: pretraining on over 30,000 simulated spectra generated using the transfer matrix method and fine-tuning on real experimental data. This extensive simulated data set enhances the model's ability to generalize across different sensor designs, significantly improving robustness and accuracy. Rigorous cross-validation across multiple devices demonstrates that the transfer learning approach reduces etaloning-induced distortions by up to 70%, ensuring substantial spectral accuracy and interpretability improvements. This study sets a new standard for achieving reliable spectral data by combining correction procedures with physics simulations.