用于宽带相干反斯托克斯拉曼光谱（BCARS）相位 retrieval 的 iPINN：非线性光谱学中函数近似与逆建模问题的一种框架

中文摘要 AI 辅助

宽带相干反斯托克斯拉曼光谱（BCARS）中的相位 retrieval 是一个不适定逆问题。类拉曼信号编码在共振极化率的虚部中，该虚部会随采集变化的非共振背景（NRB）发生相干混合。我们引入逆物理信息神经网络（iPINN），其可从原始 BCARS 光谱预测洛伦兹峰参数，并通过可微分解析正向模型重建共振极化率。Transformer 编码器将光谱特征分配给 24 个可学习峰槽，多视图一致性损失确保其在 NRB 模式、NRB 强度和噪声下的不变性。与直接光谱回归方法不同，该方法在不同采集条件下仍保持精度。在公共基准上，iPINN 实现了测试基线中最低误差（MAE 0.016，次优为 0.046）；在跨七种溶剂和四个焦点位置采集的 28 个零样本测试光谱上，七种溶剂中有五种的精度与深度无关。这些结果表明，带可微分物理解码器的逆参数预测支持跨测量条件的鲁棒相位 retrieval。

英文摘要

Phase retrieval in broadband coherent anti-Stokes Raman spectroscopy (BCARS) is an ill-posed inverse problem. The Raman-like signal is encoded in the imaginary part of the resonant susceptibility, which mixes coherently with a non-resonant background (NRB) that varies across acquisitions. We introduce an inverse physics-informed neural network (iPINN) that predicts Lorentzian peak parameters from raw BCARS spectra and reconstructs the resonant susceptibility through a differentiable analytical forward model. A transformer encoder assigns spectral features to 24 learnable peak slots, and a multi-view consistency loss enforces invariance across NRB pattern, NRB strength, and noise. Unlike direct spectral regression approaches, the method retains accuracy under varying acquisition conditions. On a public benchmark, iPINN achieves the lowest error among the tested baselines (MAE 0.016 vs. next-best 0.046). On 28 zero-shot test spectra acquired across seven solvents and four focal positions, accuracy is depth-invariant in five of seven solvents. These results show that inverse parametric prediction with a differentiable physical decoder supports robust phase retrieval across measurement conditions.

发表机构

• Friedrich Schiller University Jena（弗里德里希·席勒大学耶拿分校）
• Leibniz Centre for Photonics in Infection Research (LPI)（莱布尼茨感染研究光子学中心）
• Leibniz Institute of Photonic Technology（莱布尼茨光子技术研究所）
• Leibniz Health Technologies（莱布尼茨健康技术中心）

机构由 AI 辅助整理，请以论文原文为准。