  Supernova (SN) plays an important role in galaxy formation and evolution. In
high-resolution galaxy simulations using massively parallel computing, short
integration timesteps for SNe are serious bottlenecks. This is an urgent issue
that needs to be resolved for future higher-resolution galaxy simulations. One
possible solution would be to use the Hamiltonian splitting method, in which
regions requiring short timesteps are integrated separately from the entire
system. To apply this method to the particles affected by SNe in a
smoothed-particle hydrodynamics simulation, we need to detect the shape of the
shell on and within which such SN-affected particles reside during the
subsequent global step in advance. In this paper, we develop a deep learning
model, 3D-MIM, to predict a shell expansion after a SN explosion. Trained on
turbulent cloud simulations with particle mass $m_{\rm gas}=1$M$_\odot$, the
model accurately reproduces the anisotropic shell shape, where densities
decrease by over 10 per cent by the explosion. We also demonstrate that the
model properly predicts the shell radius in the uniform medium beyond the
training dataset of inhomogeneous turbulent clouds. We conclude that our model
enables the forecast of the shell and its interior where SN-affected particles
will be present.
