Physics-based AI methodology for Material Parameter Extraction from Optical Data
Fuente:
arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866909533975085056 |
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| author | Koumans, M. van Mechelen, J. L. M. |
| author_facet | Koumans, M. van Mechelen, J. L. M. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_08183 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Physics-based AI methodology for Material Parameter Extraction from Optical Data Koumans, M. van Mechelen, J. L. M. Computational Physics Computer Vision and Pattern Recognition Optics 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. |
| title | Physics-based AI methodology for Material Parameter Extraction from Optical Data |
| topic | Computational Physics Computer Vision and Pattern Recognition Optics |
| url | https://arxiv.org/abs/2503.08183 |