  We extend a recently proposed machine-learning-based iterative solver, i.e.
the hybrid iterative transferable solver (HINTS), to solve the scattering
problem described by the Helmholtz equation in an exterior domain with a
complex absorbing boundary condition. The HINTS method combines neural
operators (NOs) with standard iterative solvers, e.g. Jacobi and Gauss-Seidel
(GS), to achieve better performance by leveraging the spectral bias of neural
networks. In HINTS, some iterations of the conventional iterative method are
replaced by inferences of the pre-trained NO. In this work, we employ HINTS to
solve the scattering problem for both 2D and 3D problems, where the standard
iterative solver fails. We consider square and triangular scatterers of various
sizes in 2D, and a cube and a model submarine in 3D. We explore and illustrate
the extrapolation capability of HINTS in handling diverse geometries of the
scatterer, which is achieved by training the NO on non-scattering scenarios and
then deploying it in HINTS to solve scattering problems. The accurate results
demonstrate that the NO in HINTS method remains effective without retraining or
fine-tuning it whenever a new scatterer is given. Taken together, our results
highlight the adaptability and versatility of the extended HINTS methodology in
addressing diverse scattering problems.
