  We report a computational strategy to obtain the charges of individual
dielectric particles from experimental observation of their interactions as a
function of time. This strategy uses evolutionary optimization to minimize the
difference between trajectories extracted from experiment and simulated
trajectories based on many-particle force fields. The force fields include both
Coulombic interactions and dielectric polarization effects that arise due to
particle-particle charge mismatch and particle-environment dielectric contrast.
The strategy was applied to systems of free falling charged granular particles
in vacuum, where electrostatic interactions are the only driving forces that
influence the particles' motion. We show that when the particles' initial
positions and velocities are known, the optimizer requires only an initial and
final particle configuration of a short trajectory in order to accurately infer
the particles' charges; when the initial velocities are unknown and only the
initial positions are given, the optimizer can learn from multiple frames along
the trajectory to determine the particles' initial velocities and charges.
While the results presented here offer a proof-of-concept demonstration of the
proposed ideas, the proposed strategy could be extended to more complex systems
of electrostatically charged granular matter.
