  We describe how to apply adjoint sensitivity methods to backward Monte-Carlo
schemes arising from simulations of particles passing through matter. Relying
on this, we demonstrate derivative based techniques for solving inverse
problems for such systems without approximations to underlying transport
dynamics. We are implementing those algorithms for various scenarios within a
general purpose differentiable programming C++17 library NOA
(github.com/grinisrit/noa).

Computational Physics;Data Analysis, Statistics and Probability