Uncertainty-aware Physics-informed Neural Networks for Robust CARS-to-Raman Signal Reconstruction

TL;DR

This paper evaluates uncertainty quantification methods in physics-informed neural networks for reconstructing Raman spectra from CARS data, emphasizing improved calibration and reliability in scientific applications.

Contribution

It introduces and compares UQ techniques within physics-informed neural networks for CARS-to-Raman reconstruction, enhancing trustworthiness of the results.

Findings

Physics-informed models improve calibration.

Uncertainty quantification enhances reliability.

Comparison of UQ methods identifies best approaches.

Abstract

Coherent anti-Stokes Raman scattering (CARS) spectroscopy is a powerful and rapid technique widely used in medicine, material science, and chemical analyses. However, its effectiveness is hindered by the presence of a non-resonant background that interferes with and distorts the true Raman signal. Deep learning methods have been employed to reconstruct the true Raman spectrum from measured CARS data using labeled datasets. A more recent development integrates the domain knowledge of Kramers-Kronig relationships and smoothness constraints in the form of physics-informed loss functions. However, these deterministic models lack the ability to quantify uncertainty, an essential feature for reliable deployment in high-stakes scientific and biomedical applications. In this work, we evaluate and compare various uncertainty quantification (UQ) techniques within the context of CARS-to-Raman signal reconstruction. Furthermore, we demonstrate that incorporating physics-informed constraints into these models improves their calibration, offering a promising path toward more trustworthy CARS data analysis.