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

ANN-MoC Method for Inverse Transient Transport Problems in One-Dimensional Geometry