Enhancing analytical photon transport in turbid media through physics-guided neural network corrections
A physics-guided residual learning framework that combines the computational efficiency of analytical modeling with the accuracy of MC simulation is introduced and it is demonstrated that learning corrections to an analytical physical baseline improves both prediction accuracy and data efficiency.
Guo-Jun Tang, P. Teal, Alexander Doronin
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