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

2025-11-17

AI Summary

CARS spectral reconstruction suffers from distortion due to non-resonant background interference, and existing deterministic models lack the capability to quantify predictive uncertainty—hindering trustworthy deployment in high-stakes biomedical applications. To address this, we propose a physics-informed neural network (PINN) framework that jointly incorporates physical priors and uncertainty quantification (UQ). Specifically, we integrate Kramers–Kronig relations and smoothness constraints into a physics-informed loss function, and couple a Bayesian neural network with Monte Carlo Dropout to yield well-calibrated uncertainty estimates. Experiments demonstrate that physics-based regularization substantially enhances UQ reliability. Our method outperforms purely data-driven models in reconstruction accuracy, robustness against background contamination, and confidence calibration. This work establishes a trustworthy, end-to-end solution for CARS spectral reconstruction, enabling broader clinical and scientific adoption.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Relational Probabilistic Models
• Computer Vision: Low Level & Physics-based Vision

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Psychology-informed user models and recommender systems

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.

Problem

• Reconstructing Raman signals from CARS data with non-resonant background interference
• Addressing uncertainty quantification limitations in deterministic physics-informed models
• Improving calibration of deep learning models using physics-informed constraints

Innovation

• Uncertainty-aware physics-informed neural networks
• Integrating Kramers-Kronig relationships as constraints
• Improved calibration through physics-informed uncertainty quantification