Physics-based AI methodology for Material Parameter Extraction from Optical Data
physics.comp-ph, cs.CV, physics.optics
Submitted: 2025-03-11
Updated: 2025-03-11
Comments: Submitted for IRMMW-THz 2025 conference proceedings
Journal ref: 2025 50th International Conference on Infrared, Millimeter, and Terahertz Waves (IRMMW-THz), Helsinki, Finland, 2025, pp. 1-2
DOI: 10.1109/IRMMW-THz61557.2025.11320072
License: http://creativecommons.org/licenses/by/4.0/
The gist: We report on a novel methodology for extracting material parameters from spectroscopic optical data using a physics-based neural network.
Abstract
We report on a novel methodology for extracting material parameters from spectroscopic optical data using a physics-based neural network. The proposed model integrates classical optimization frameworks with a multi-scale object detection framework, specifically exploring the effect of incorporating physics into the neural network. We validate and analyze its performance on simulated transmission spectra at terahertz and infrared frequencies. Compared to traditional model-based approaches, our method is designed to be autonomous, robust, and time-efficient, making it particularly relevant for industrial and societal applications.
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