  In confined systems, such as the inside of a biological cell, the outer
boundary or wall can affect the dynamics of internal particles. In many cases
of interest both the internal particle and outer wall are approximately
spherical. Therefore, quantifying the wall effects from an outer spherical
boundary on the motion of an internal eccentric sphere is very useful. However,
when the two spheres are not concentric, the problem becomes non-trivial. In
this paper we improve existing analytical methods to evaluate these wall
effects and then train a feed-forward artificial neural network within a
broader model. The final model generally performed with $\sim 0.001\%$ error
within the training domain and $\sim 0.05\%$ when the outer spherical wall was
extrapolated to an infinite plane. Through this model, the wall effects of an
outer spherical boundary on the arbitrary motion of an internal sphere for all
experimentally achievable configurations can now be conveniently and
efficiently determined.
