用小波流匹配加速宇宙早期结构重建，采样快46倍。
Cosmo3DFlow: Wavelet Flow Matching for Spatial-to-Spectral Compression in Reconstructing the Early Universe
- 结合3D离散小波变换与流匹配，压缩时空数据
- 在128³模拟下采样速度提升46倍，秒级生成初始条件
- 适合需要快速宇宙演化模拟的研究者
从当前宇宙反推早期宇宙结构是现代天体物理学中一项极具挑战性且计算密集的任务。我们提出一种新型生成框架Cosmo3DFlow，旨在解决维度高和稀疏性两大瓶颈。通过将3D离散小波变换（DWT）与流匹配相结合，有效表征高维宇宙结构。小波变换将空间空洞转化为频谱稀疏，分离高频细节与低频结构，使小波域速度场支持大步长的稳定常微分方程（ODE）求解器。基于128³分辨率的大规模宇宙N体模拟，相比扩散模型，采样速度最高提升46倍，初始条件可在秒级生成，而以往方法需数分钟。
原文摘要 · Abstract (English)
Reconstructing the early universe from the evolved present-day universe is a challenging and computationally demanding problem in modern astrophysics. We devise a novel generative framework, Cosmo3DFlow, designed to address dimensionality and sparsity, the critical bottlenecks inherent in current state-of-the-art methods for cosmological inference. By integrating 3D Discrete Wavelet Transform (DWT) with flow matching, we effectively represent high-dimensional cosmological structures. The Wavelet Transform addresses the ``void problem'' by translating spatial emptiness into spectral sparsity. It decouples high-frequency details from low-frequency structures, and wavelet-space velocity fields facilitate stable ordinary differential equation (ODE) solvers with large step sizes. Using large-scale cosmological $N$-body simulations at $128^3$ resolution, we achieve up to $46\times$ faster sampling than diffusion models. Our results enable initial conditions to be sampled in seconds, compared to minutes for previous methods.
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