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

## TL;DR

This paper introduces a new method using physics-based simulations and deep learning to remove distortions in spectroscopic data.

## Contribution

A novel inverse modeling framework combining computational physics and transfer learning for artifact removal in photonic data.

## Key findings

- A two-phase transfer learning strategy improves model generalization across sensor designs.
- The method reduces etaloning distortions by up to 70% in real-world data.
- Physics-based simulations significantly enhance the accuracy of spectral data correction.

## Abstract

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.