  We present JefiAtten, a novel neural network model employing the attention
mechanism to solve Maxwell's equations efficiently. JefiAtten uses
self-attention and cross-attention modules to understand the interplay between
charge density, current density, and electromagnetic fields. Our results
indicate that JefiAtten can generalize well to a range of scenarios,
maintaining accuracy across various spatial distribution and handling amplitude
variations. The model showcases an improvement in computation speed after
training, compared to traditional integral methods. The adaptability of the
model suggests potential for broader applications in computational physics,
with further refinements to enhance its predictive capabilities and
computational efficiency. Our work is a testament to the efficacy of
integrating attention mechanisms with numerical simulations, marking a step
forward in the quest for data-driven solutions to physical phenomena.
