Physics‐Guided Monte Carlo Surrogate Modeling for Optical Properties Inference and Spectral Prediction From Limited Experimental Data

Abstract

ABSTRACT We present a physics‐guided neural framework that combines Monte Carlo simulations with experimental measurements to characterize optical properties and predict spectral responses at unseen conditions. The hybrid model jointly learns from simulations with complete optical characterization and experimental data where properties must be inferred through photon diffusion constrained embeddings, enabling knowledge transfer from synthetic to real materials. Applied to transparent wood composites, the framework characterizes wavelength‐dependent effective attenuation from only two experimental samples and accurately predicts optical responses at a third unseen thickness across all measured spectral quantities. Controlled validation on Monte Carlo simulations demonstrates that physics‐guided training reduces prediction errors by at unseen sample thicknesses compared to data‐driven approaches, preventing systematic spectral biases without compromising training performance. This framework enables accurate and non‐destructive material characterization from minimal experimental measurements, reducing the cost and time required for optical property determination across diverse sample configurations.