[2510.06020] RamPINN: Recovering Raman Spectra From Coherent Anti-Stokes Spectra Using Embedded Physics

Computer Science > Machine Learning arXiv:2510.06020 (cs) [Submitted on 7 Oct 2025 (v1), last revised 25 Mar 2026 (this version, v2)] Title:RamPINN: Recovering Raman Spectra From Coherent Anti-Stokes Spectra Using Embedded Physics Authors:Sai Karthikeya Vemuri, Adithya Ashok Chalain Valapil, Tim Büchner, Joachim Denzler View a PDF of the paper titled RamPINN: Recovering Raman Spectra From Coherent Anti-Stokes Spectra Using Embedded Physics, by Sai Karthikeya Vemuri and 3 other authors View PDF Abstract:Transferring the recent advancements in deep learning into scientific disciplines is hindered by the lack of the required large-scale datasets for training. We argue that in these knowledge-rich domains, the established body of scientific theory provides reliable inductive biases in the form of governing physical laws. We address the ill-posed inverse problem of recovering Raman spectra from noisy Coherent Anti-Stokes Raman Scattering (CARS) measurements, as the true Raman signal here is suppressed by a dominating non-resonant background. We propose RamPINN, a model that learns to recover Raman spectra from given CARS spectra. Our core methodological contribution is a physics-informed neural network that utilizes a dual-decoder architecture to disentangle resonant and non-resonant signals. This is done by enforcing the Kramers-Kronig causality relations via a differentiable Hilbert transform loss on the resonant and a smoothness prior on the non-resonant part of the signal. ...