@inproceedings{venkataramanan2025uncertainty,
    type = {inproceedings},
    key = {venkataramanan2025uncertainty},
    author = {Aishwarya Venkataramanan and Sai Karthikeya Vemuri and Adithya Ashok Chalain Valapil and Joachim Denzler},
    title = {Uncertainty-aware Physics-informed Neural Networks for Robust CARS-to-Raman Signal Reconstruction},
    booktitle = {EurIPS Workshop on Differentiable Systems and Scientific Machine Learning (EurIPS-WS)},
    year = {2025},
    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.},
    note = {(accepted)},
    groups = {uncertainty},
}