arXiv Machine Learning September 2, 2026 By Ravi Teja Vulchi, Carl Messerschmidt, Mohammadsadegh Vafaeinezhad, Rajendhar Junjuri, Tobias Meyer-Zedler, Juergen Popp, Thomas Bocklitz

iPINN for Broadband CARS Phase Retrieval: A Framework for Function Approximation and Inverse Modeling Problems in Nonlinear Spectroscopy

Read the original on arXiv Machine Learning →

The paper presents iPINN, an inverse physics‑informed neural network designed for broadband coherent anti‑Stokes Raman spectroscopy (BCARS) phase retrieval. iPINN predicts Lorentzian peak parameters from raw BCARS spectra and reconstructs the resonant susceptibility using a differentiable analytical forward model, employing a transformer encoder and a multi‑view consistency loss to handle varying non‑resonant background conditions. On a public benchmark it achieves the lowest mean absolute error (0.016) compared to other methods, and demonstrates depth‑invariant accuracy across multiple solvents and focal positions.

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