Hyperspectral absorption tomography has emerged as a promising technique for combustion diagnostics due to its rich spectral measurements. However, the non-linear and ill-posed nature of the inverse problem makes obtaining accurate results challenging. This paper proposes a novel application of a physics-informed neural network to address the non-linear inverse problem in hyperspectral absorption spectroscopy. This method utilizes physical laws and measurement data to guide the neural network in finding the optimal solution, without requiring training data. To demonstrate its capabilities, the physics-informed neural network is employed to retrieve temperature and CO2 mole fraction fields in axisymmetric laminar diffusion flames via 4.3μm TDLAS (tunable diode laser absorption spectroscopy). The developed neural network is applied to resolve the spatial distributions from the spectral dimensions, requiring fewer spatial measurements for directly retrieving temperature and CO2 mole fraction profiles. We investigate the minimum radial projections needed for accurate retrievals and evaluate the model’s robustness to random noise through the inversion of a simulated flame. The developed model is further applied to reconstruct the temperature and CO2 mole fraction fields for an experimentally measured flame. Our results demonstrate that the proposed model maintains high retrieval accuracy even with limited, noisy data. This work highlights the potential of the physics-informed neural network for robust solutions to non-linear laser absorption tomography problems in scientific and engineering applications.

中文翻译：

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用于非线性激光吸收断层扫描的物理信息神经网络


高光谱吸收断层扫描由于其丰富的光谱测量功能，已成为一种很有前途的燃烧诊断技术。然而，逆问题的非线性和病态性质使得获得准确的结果具有挑战性。本文提出了一种物理信息神经网络的新应用，以解决高光谱吸收光谱中的非线性逆问题。此方法利用物理定律和测量数据来指导神经网络找到最佳解决方案，而无需训练数据。为了证明其功能，采用物理信息神经网络通过 4.3μm TDLAS（可调谐二极管激光吸收光谱）检索轴对称层流扩散火焰中的温度和 CO2 摩尔分数场。开发的神经网络用于从光谱维度解析空间分布，需要更少的空间测量来直接检索温度和 CO2 摩尔分数分布。我们研究了准确检索所需的最小径向投影，并通过模拟火焰的反转评估了模型对随机噪声的鲁棒性。开发的模型进一步应用于重建实验测量火焰的温度和 CO2 摩尔分数场。我们的结果表明，即使数据有限、嘈杂，所提出的模型也能保持较高的检索准确性。这项工作强调了物理信息神经网络在科学和工程应用中为非线性激光吸收断层扫描问题提供稳健解决方案的潜力。