论文标题
使用深物理模型的宇宙学模拟的超分辨率模拟器
Super-resolution emulator of cosmological simulations using deep physical models
论文作者
论文摘要
我们提出了我们最近开发的WASSERTEAN优化模型的扩展，以模拟低分辨率宇宙学模拟的精确高分辨率特征。我们的深层物理建模技术依赖于受限制的神经网络来对低分辨率宇宙密度场的分布进行映射到高分辨率小规模结构的空间。我们使用高分辨率初始条件的单个三联体以及相应的高分辨率和高分辨率进化的暗物质模拟来限制我们的网络。我们利用高分辨率初始条件的信息内容作为一个构建精良的先前分布，网络从中模仿了小规模的结构。一旦安装，我们的物理模型就以低计算成本模仿了高分辨率模拟，同时还提供了一些有关大型模式如何影响实际空间中小规模结构的见解。
We present an extension of our recently developed Wasserstein optimized model to emulate accurate high-resolution features from computationally cheaper low-resolution cosmological simulations. Our deep physical modelling technique relies on restricted neural networks to perform a mapping of the distribution of the low-resolution cosmic density field to the space of the high-resolution small-scale structures. We constrain our network using a single triplet of high-resolution initial conditions and the corresponding low- and high-resolution evolved dark matter simulations from the Quijote suite of simulations. We exploit the information content of the high-resolution initial conditions as a well constructed prior distribution from which the network emulates the small-scale structures. Once fitted, our physical model yields emulated high-resolution simulations at low computational cost, while also providing some insights about how the large-scale modes affect the small-scale structure in real space.
