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Supernova (SN) plays an important role in galaxy formation and evolution. Inhigh-resolution galaxy simulations using massively parallel computing, shortintegration timesteps for SNe are serious bottlenecks. This is an urgent issuethat needs to be resolved for future higher-resolution galaxy simulations. Onepossible solution would be to use the Hamiltonian splitting method, in whichregions requiring short timesteps are integrated separately from the entiresystem. To apply this method to the particles affected by SNe in asmoothed-particle hydrodynamics simulation, we need to detect the shape of theshell on and within which such SN-affected particles reside during thesubsequent global step in advance. In this paper, we develop a deep learningmodel, 3D-MIM, to predict a shell expansion after a SN explosion. Trained onturbulent cloud simulations with particle mass $m_{\rm gas}=1$M$_\odot$, themodel accurately reproduces the anisotropic shell shape, where densitiesdecrease by over 10 per cent by the explosion. We also demonstrate that themodel properly predicts the shell radius in the uniform medium beyond thetraining dataset of inhomogeneous turbulent clouds. We conclude that our modelenables the forecast of the shell and its interior where SN-affected particleswill be present.

3D-Spatiotemporal Forecasting the Expansion of Supernova Shells Using Deep Learning toward High-Resolution Galaxy Simulations