A GENERAL NONLINEAR INVERSE TRANSPORT ALGORITHM USING FORWARD AND ADJOINT FLUX COMPUTATIONS

Iterative approaches to the nonlinear inverse transport problem are de scribed, which give rise to the structure that best predicts a set of transport observations, Such methods are based on minimizing a global error functional measuring the discrepancy between predicted and obser ved transport data, Required for this minimization is the functional g radient (Frechet derivative) of the global error evaluated with respec t to a set of unknown material parameters (specifying boundary locatio ns, scattering cross sections, etc.) which are to be determined, It is shown how this functional gradient is obtained from numerical solutio ns to the forward and adjoint transport problems computed once per ite ration, This approach is not only far more efficient, but also more ac curate, than a finite-difference method for computing the gradient of the global error, The general technique can be applied to inverse-tran sport problems of all descriptions, provided only that solutions to th e forward and adjoint problems can be found numerically, As an illustr ation, two inverse problems are treated: the reconstruction of an anis otropic scattering function in a one-dimensional homogeneous slab and the two-dimensional ''imaging'' of a spatially-varying scattering cros s section.