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.

翻译：超新星在星系形成与演化中扮演重要角色。在大规模并行计算的高分辨率星系模拟中，超新星所需的短积分时间步长构成严重瓶颈，这是未来更高分辨率星系模拟亟待解决的紧迫问题。一种可行的解决方案是采用哈密顿分裂方法，将需要短时间步长的区域与整个系统分开积分。要将该方法应用于平滑粒子流体动力学模拟中受超新星影响的粒子，需要预先检测这些粒子在后续全局时间步内所处的壳层形状及内部结构。本文开发了深度学习模型3D-MIM，用于预测超新星爆发后的壳层膨胀。通过训练粒子质量$m_{\rm gas}$~=~1 M$_\odot$的湍流云模拟数据，该模型准确再现了各向异性的壳层形状，其中密度因爆发而降低超过10%。我们进一步证明，该模型能在超出训练数据集（非均匀湍流云）范围的均匀介质中正确预测壳层半径。结论表明，该模型能够预报超新星影响粒子所在壳层及其内部区域。