AI 中文总结
本研究提出Cosmo-SPINN框架，采用物理信息生成U-Net模型，解决模糊暗物质模拟的演化与超分辨率任务，可在减少训练数据的同时提升模拟质量，有效减少生成伪影并保持物理一致性。
AI 中文摘要
生成式机器学习模型近年来已成为生成宇宙学模拟的强大工具。然而，许多现有模拟器并未明确强制执行支配宇宙演化的基础物理动力学，常导致伪影且难以满足演化方程的要求。本研究提出一种用于模糊暗物质（FDM）的物理信息生成U-Net框架，可解决两项互补任务：（i）宇宙学场从初始条件到任意宇宙学尺度因子的演化；（ii）特定宇宙学尺度因子下FDM模拟的超分辨率。我们的模型采用物理信息损失函数，在训练过程中明确确保与基础薛定谔-泊松（SP）动力学的一致性。对于演化任务，我们发现即使仅使用少量训练数据，加入这种基于物理的损失也能显著提升预测模拟的质量；仅使用20%的训练数据时，模型可在1 h⁻¹ Mpc的模拟盒中准确复现目标模拟，且能有效泛化至先前未见过的初始条件实现。对于超分辨率任务，我们首次提出一种基于求解完整SP方程得到的FDM模拟训练的生成式超分辨率模型，同时考虑初始条件的单一及多种实现，并分析每种情况下物理信息损失的作用。我们的方法可实现宇宙学模拟的现代生成式建模，同时保持物理一致性并大幅减少生成伪影。
英文摘要
Generative machine learning models have recently emerged as powerful tools for producing cosmological simulations. However, many existing emulators do not explicitly enforce the underlying physical dynamics governing cosmological evolution, often leading to artifacts and poor adherence to the evolution equations. In this work, we present a physics-informed generative U-Net framework for fuzzy dark matter (FDM) that addresses two complementary tasks: (i) the evolution of cosmological fields from initial conditions to an arbitrary cosmological scale factor and (ii) the super-resolution of FDM simulations at a specified cosmological scale factor. Our model incorporates a physics-informed loss function that explicitly enforces consistency with the underlying Schrödinger-Poisson (SP) dynamics during training. For the evolution task, we find that the inclusion of this physics-based loss significantly improves the quality of the predicted simulations, even when only a small amount of training data is available. Using only 20% of the training data, the model accurately reproduces the target simulations in a 1 $h^{-1}$ Mpc box. Furthermore, the framework generalizes effectively across previously unseen realizations of the initial conditions. For the super-resolution task, we present, for the first time, a generative super-resolution model trained on FDM simulations obtained by solving the full SP equations, considering both single and multiple realizations of the initial conditions and analyzing the role of the physics-informed loss in each case. Our approach enables modern generative modeling of cosmological simulations while maintaining physical consistency and substantially reducing generative artifacts.
Comments21 pages, 7 figures (2 more in Appendix), submitted to ApJ; comments welcome
