The inverse problems of particle neutral transport models have many important engineering and medical applications. Safety protocols, quality control procedures, and optical medical solutions can be developed based on inverse transport solutions. In this work, we propose the ANN-MoC method to solve the inverse transient transport problem of estimating the absorption coefficient from measurements of the scalar flux at the boundaries of the model domain. The main idea is to train an Artificial Neural Network (ANN) from data generated by direct solutions computed by a Method of Characteristics (MoC) solver. The direct solver is tested on a problem with a manufactured solution. And, the proposed ANN-MoC method is tested on two inverse problems. In the first, the medium is homogeneous and has a constant absorption coefficient. In the second, a heterogeneous medium is considered, with the absorption coefficient constant by parts. Very accurate ANN estimations have been achieved for these two problems, indicating that the quality of the results relies on the accuracy of the direct solver solutions. The results show the potential of the proposed approach to be applied to more realistic inverse transport problems.

翻译：逆粒子中性输运模型在工程和医学领域具有重要应用，基于逆输运解可开发安全规程、质量控制程序及光学医疗解决方案。本文提出ANN-MoC方法，通过边界标量通量测量求解瞬态逆输运问题中吸收系数的估计。核心思想是利用特征线法（MoC）求解器生成的直接解训练人工神经网络（ANN）。直接求解器在具有解析解的问题上进行了验证，所提出的ANN-MoC方法在两类逆问题上进行了测试：第一类为均匀介质中恒定吸收系数的情况，第二类为分段恒定吸收系数的非均匀介质。对这两个问题均实现了高精度ANN估计，表明结果质量取决于直接求解器解的精度。研究成果展示了该方法的潜力，可应用于更真实的逆输运问题。