Uncertainty-aware Physics-informed Neural Networks for Robust CARS-to-Raman Signal Reconstruction
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866915622477103104 |
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| author | Venkataramanan, Aishwarya Vemuri, Sai Karthikeya Valapil, Adithya Ashok Chalain Denzler, Joachim |
| author_facet | Venkataramanan, Aishwarya Vemuri, Sai Karthikeya Valapil, Adithya Ashok Chalain Denzler, Joachim |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_13185 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Uncertainty-aware Physics-informed Neural Networks for Robust CARS-to-Raman Signal Reconstruction Venkataramanan, Aishwarya Vemuri, Sai Karthikeya Valapil, Adithya Ashok Chalain Denzler, Joachim Machine Learning 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. |
| title | Uncertainty-aware Physics-informed Neural Networks for Robust CARS-to-Raman Signal Reconstruction |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2511.13185 |