Abstract In this article, a multilayer artificial neural network (ANN) identification model is developed to simultaneously identify the thermal conductivity and the effective absorption coefficient of semitransparent materials from flash-type experimental measurements. Firstly, the ANN is trained by means of data generated by a 2-D axisymmetric heat transfer model whose radiative part is treated via the P1 approximation. A sensitivity study is then used to prove the theoretical feasibility of the identification strategy. Several training data distributions (uniform or Gaussian types) are tested on synthetic data, and on noisy ones for checking the robustness. Finally, the efficiency of this estimation approach is investigated using experimental data obtained by flash method on a PMMA sample. The estimated thermal conductivity and the effective absorption coefficient are compared with values obtained from the literature and other measurements.

中文翻译：

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使用由二维轴对称直接模型训练的人工神经网络同时识别半透明介质的热物理特性

摘要 在本文中，开发了一种多层人工神经网络 (ANN) 识别模型，以通过闪光型实验测量同时识别半透明材料的热导率和有效吸收系数。首先，人工神经网络通过二维轴对称传热模型生成的数据进行训练，该模型的辐射部分通过 P1 近似处理。然后使用敏感性研究来证明识别策略的理论可行性。几种训练数据分布（均匀或高斯类型）在合成数据和噪声数据上进行测试，以检查稳健性。最后，使用通过闪光法在 PMMA 样品上获得的实验数据来研究这种估计方法的效率。