  Accurate estimation of the sound field around a rigid sphere necessitates
adequate sampling on the sphere, which may not always be possible. To overcome
this challenge, this paper proposes a method for sound field estimation based
on a physics-informed neural network. This approach integrates physical
knowledge into the architecture and training process of the network. In
contrast to other learning-based methods, the proposed method incorporates
additional constraints derived from the Helmholtz equation and the zero radial
velocity condition on the rigid sphere. Consequently, it can generate
physically feasible estimations without requiring a large dataset. In contrast
to the spherical harmonic-based method, the proposed approach has better
fitting abilities and circumvents the ill condition caused by truncation.
Simulation results demonstrate the effectiveness of the proposed method in
achieving accurate sound field estimations from limited measurements,
outperforming the spherical harmonic method and plane-wave decomposition
method.
