论文标题
行星形成早期阶段的神经网络亚网格模型
A Neural Network Subgrid Model of the Early Stages of Planet Formation
论文作者
论文摘要
Planet组是一个多尺度的过程，其中$ \ Mathrm {μm} $ - 大小的原始磁盘中的粉尘晶粒受到天文单位尺度上的流体动力过程的强烈影响（$ \ $ \ \ \ \ \ \ \ \ \ \ \ \ m m iathrm约1.5 \ times 10^8 \，\ mathrm} $ {km} $）。因此，研究取决于亚网格模型，以模仿大规模流体动力模拟的顶部粉尘凝结的微物理。包括相关物理效果的数值模拟很复杂且计算昂贵。在这里，我们提出了一个快速准确的学习有效模型，用于粉尘凝血，该模型对高分辨率数值凝结模拟的数据进行了训练。我们的模型捕获了迄今为止与其他具有相似计算效率的灰尘凝血处方的粉尘凝结过程的细节。
Planet formation is a multi-scale process in which the coagulation of $\mathrm{μm}$-sized dust grains in protoplanetary disks is strongly influenced by the hydrodynamic processes on scales of astronomical units ($\approx 1.5\times 10^8 \,\mathrm{km}$). Studies are therefore dependent on subgrid models to emulate the micro physics of dust coagulation on top of a large scale hydrodynamic simulation. Numerical simulations which include the relevant physical effects are complex and computationally expensive. Here, we present a fast and accurate learned effective model for dust coagulation, trained on data from high resolution numerical coagulation simulations. Our model captures details of the dust coagulation process that were so far not tractable with other dust coagulation prescriptions with similar computational efficiency.
